Presenter: Miroslav Skácel
BUT Zero-Cost Speech Recognition 2016 System Description In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Miroslav Skácel, Martin Karafiát, Lucas Ondel, Albert Uchytil, Igor Szöke
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_48.pdf
Video: https://youtu.be/0pNiLLVTa28
Abstract: This paper describes our work on developing speech recognizers for Vietnamese. It focuses on procedures to prepare provided data precisely. We aim on analysis of the textual transcriptions in particular. Methods to filter out defective data to improve performance of final system are proposed and described in detail. We also propose cleaning of other textual data used for language modeling. Several architectures are investigated to reach both sub-tasks goals. The achieved results are discussed.
MediaEval 2016 - ININ Submission to Zero Cost ASR Taskmultimediaeval
Presenter: Tejas Godambe
ININ Submission to Zero Cost ASR Task at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Tejas Godambe, Naresh Kumar, Pavan Kumar, Veera Raghavendra, Aravind Ganapathiraju
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_31.pdf
Video: https://youtu.be/e70xRjsUUts
Abstract: This paper details the experiments conducted to train an as good performing Vietnamese speech recognition system as possible using public domain data only, as a part of the Zero Cost task at MediEval 2016. We explored techniques related to audio preprocessing, use of speaker’s pitch information, data perturbation, for building subspace Gaussian mixture acoustic model which is known for estimating robust parameters when the amount of data is less, and also unsupervised adaptation, RNN language model based lattice rescoring and system combination using ROVER technique.
MediaEval 2016 - UNIFESP Predicting Media Interestingness Taskmultimediaeval
Presenter: Samuel G. Fadel
UNIFESP at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Jurandy Almeida
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_28.pdf
Video: https://youtu.be/YLthKNczlcA
Abstract: This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.
MediaEval 2016 - HUCVL Predicting Interesting Key Frames with Deep Modelsmultimediaeval
Presenter: Göksu Erdoğan
HUCVL at MediaEval 2016: Predicting Interesting Key Frames with Deep Models In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Goksu Erdogan, Aykut Erdem, Erkut Erdem
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_18.pdf
Video: https://youtu.be/A--6O2v81Cw
Abstract: In MediaEval 2016, we focus on the image interestingness subtask which involves predicting interesting key frames of a video in the form of a movie trailer. We specifically propose three different deep models for this subtask. The first two models are based on fine-tuning two pretrained models, namely AlexNet and MemNet, where we cast the interestingness prediction as a regression problem. Our third deep model, on the other hand, depends on a triplet network which is comprised of three instances of the same feedforward network with shared weights, and trained according to a triplet ranking loss. Our experiments demonstrate that all these models provide relatively similar and promising results on the image interestingness subtask.
MediaEval 2016 - ININ Submission to Zero Cost ASR Taskmultimediaeval
Presenter: Tejas Godambe
ININ Submission to Zero Cost ASR Task at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Tejas Godambe, Naresh Kumar, Pavan Kumar, Veera Raghavendra, Aravind Ganapathiraju
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_31.pdf
Video: https://youtu.be/e70xRjsUUts
Abstract: This paper details the experiments conducted to train an as good performing Vietnamese speech recognition system as possible using public domain data only, as a part of the Zero Cost task at MediEval 2016. We explored techniques related to audio preprocessing, use of speaker’s pitch information, data perturbation, for building subspace Gaussian mixture acoustic model which is known for estimating robust parameters when the amount of data is less, and also unsupervised adaptation, RNN language model based lattice rescoring and system combination using ROVER technique.
MediaEval 2016 - UNIFESP Predicting Media Interestingness Taskmultimediaeval
Presenter: Samuel G. Fadel
UNIFESP at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Jurandy Almeida
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_28.pdf
Video: https://youtu.be/YLthKNczlcA
Abstract: This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.
MediaEval 2016 - HUCVL Predicting Interesting Key Frames with Deep Modelsmultimediaeval
Presenter: Göksu Erdoğan
HUCVL at MediaEval 2016: Predicting Interesting Key Frames with Deep Models In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Goksu Erdogan, Aykut Erdem, Erkut Erdem
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_18.pdf
Video: https://youtu.be/A--6O2v81Cw
Abstract: In MediaEval 2016, we focus on the image interestingness subtask which involves predicting interesting key frames of a video in the form of a movie trailer. We specifically propose three different deep models for this subtask. The first two models are based on fine-tuning two pretrained models, namely AlexNet and MemNet, where we cast the interestingness prediction as a regression problem. Our third deep model, on the other hand, depends on a triplet network which is comprised of three instances of the same feedforward network with shared weights, and trained according to a triplet ranking loss. Our experiments demonstrate that all these models provide relatively similar and promising results on the image interestingness subtask.
Video Retrieval for Multimedia Verification of Breaking News on Social NetworksInVID Project
This slideset presents an approach to automatically detecting breaking news events from social media streams, using event detection to collecting near real time relevant video documents from social networks regarding that breaking news. A visual analytics dashboard provides access to the results of the content processing pipeline, providing a rich interactive interface to explore emerging stories and select video material around those stories for verification.
MediaEval 2015 - JRS at Synchronization of Multi-user Event Media Taskmultimediaeval
The event synchronisation task addresses the problem of aligning media (i.e., photo and video) streams (“galleries”) from different users temporally and identifying coherent events in the streams. Our approach uses the visual similarity of image/key frame pairs based on full matching of SIFT descriptors with geometric verification. Based on the visual similarity and the given time information, a probabilistic algorithm is employed, where in each run a hypothesis is calculated for the set of time offsets with respect to the reference gallery. From the gathered hypotheses, the final set of time offsets is calculated as the medoid of all hypotheses.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2015 - CERTH at MediaEval 2015 Synchronization of Multi-User Event ...multimediaeval
This paper describes the results of our participation to the Synchronization of Multi-User Event Media Task at the MediaEval 2015 challenge. Using multiple similarity measures, we identify pairs of similar media from different galleries. We use a graph-based approach to temporally synchronize user galleries; subsequently we use time information, geolocation information and visual concept detection results to cluster all photos into different sub-events. Our method achieves good accuracy on considerably diverse datasets.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2015 - GTM-UVigo Systems for Person Discovery Task at MediaEval 2015multimediaeval
In this paper, we present the systems developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2015. The systems propose two different strategies for person discovery in audio through speaker diarization (one based on an online clustering strategy with error correction using OCR information and the other based on agglomerative hierarchical clustering) as well as intrashot and intershot trategies for face clustering.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - TUD-MMC Predicting media Interestingness Taskmultimediaeval
Presenter: Cynthia Liem
TUD-MMC at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cynthia Liem
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_30.pdf
Video: https://youtu.be/NQan10E_-kE
Abstract: This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media Interestingness Task. Noting that the nature of movie trailer shots is different from that of preceding tasks on image and video interestingness, we propose two baseline heuristic approaches based on the clear occurrence of people. MAP scores obtained on the development set and test set suggest that our approaches cover a limited but non-marginal subset of the interestingness spectrum. Most strikingly, our obtained scores on the Image and Video Subtasks are comparable or better than those obtained when evaluating the ground truth annotations of the Image Subtask against the Video Subtask and vice versa
MediaEval 2015 - Verifying Multimedia Use at MediaEval 2015multimediaeval
This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2015 MediaEval Benchmark. The task deals with the automatic detection of manipulation and misuse of Web multimedia content. Its aim is to lay the basis for a future generation of tools that could assist media professionals in the process of verification. Examples of manipulation include maliciously tampering with images and videos, e.g., splicing, removal/addition of elements, while other kinds of misuse include the reposting of previously captured multimedia content in a different context (e.g., a new event) claiming that it was captured there. For the 2015 edition of the task, we have generated and made available a large corpus of real-world cases of images that were distributed through tweets, along with manually assigned labels regarding their use, i.e. misleading (fake) versus appropriate (real).
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - Placing Images with Refined Language Models and Similarity S...multimediaeval
Presenter: Giorgos Kordopatis-Zilos
Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Giorgos Kordopatis-Zilos, Adrian Popescu, Symeon Papadopoulos, Yiannis Kompatsiaris
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_13.pdf
Video: https://youtu.be/WR4I3CWjcR4
Abstract: We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual and textual cues from the multimedia items, trained on datasets of different size and origin. The best results were obtained by a hybrid approach trained with external training data and using two publicly available gazetteers.
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Taskmultimediaeval
Presenter: Maigrot Cédric
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cédric Maigrot, Vincent Claveau, Ewa Kijak, Ronan Sicre
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_45.pdf
Video: https://youtu.be/ay1zWydnijY
Abstract: This paper presents a multi-modal hoax detection system composed of text, source, and image analysis. As hoax can be very diverse, we want to analyze several modalities to better detect them. This system is applied in the context of the Verifying Multimedia Use task of MediaEval 2016. Experiments show the performance of each separated modality as well as their combination.
MediaEval 2016 - LAPI @ 2016 Retrieving Diverse Social Images Task: A Pseudo-...multimediaeval
Presenter: Bogdan Boteanu,
LAPI @ 2016 Retrieving Diverse Social Images Task: A Pseudo-Relevance Feedback Diversification Perspective In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Bogdan Boteanu, Mihai G. Constantin, Bogdan Ionescu
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_20.pdf
Video: https://youtu.be/mDI8Z31p7TY
Abstract: In this paper we present the results achieved during the 2016 MediaEval Retrieving Diverse Social Images Task, using an approach based on pseudo-relevance feedback, in which human feedback is replaced by an automatic selection of images. The proposed approach is designed to have in priority the diversification of the results, in contrast to most of the existing techniques that address only the relevance. Diversification is achieved by exploiting a hierarchical clustering scheme followed by a diversification strategy. Methods are tested on the benchmarking data and results are analyzed. Insights for future work conclude the paper.
MediaEval 2015 - Synchronization of Multi-User Event Media at MediaEval 2015:...multimediaeval
The objective of this paper is to provide an overview of the Synchronization of Multi-User Event Media (SEM) Task, which is part of the MediaEval Benchmark for Multimedia Evaluation. The SEM task was initially presented at MediaEval in 2014, with the goal of proposing a challenge in aligning multiple users’ photo galleries related to the same event but with unreliable timestamps. Besides aligning the pictures on a common timeline, participants were also required to detect the sub-events and cluster the pictures accordingly. For 2015 we have decided to extend the task also to other types of media, thus including audio and video information for a more complete and diversified representation of the analyzed event.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - Simula Team @ Context of Experience Taskmultimediaeval
Presenter: Konstantin Pogorelov
Simula @ MediaEval 2016 Context of Experience Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Carsten Griwodz
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_53.pdf
Video: https://youtu.be/FTIeGpHhURU
Abstract: This paper presents our approach for the Context of Multimedia Experience Task of the MediaEval 2016 Benchmark. We present different analyses of the given data using different subsets of data sources and combinations of it. Our approach gives a baseline evaluation indicating that metadata approaches work well but that also visual features can provide useful information for the given problem to solve.
Media REVEALr: A social multimedia monitoring and intelligence system for Web...Symeon Papadopoulos
Presentation of Media REVEALr, a framework for mining social and Web multimedia with the goal of supporting verification. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
The InVID Plug-in: Web Video Verification on the BrowserInVID Project
Presentation of the paper "The InVID Plug-in: Web Video
Verification on the Browser" at the 1st Int. Workshop on Multimedia Verification (MuVer) that was hosted at the ACM Multimedia Conference, October 23 - 27, 2017 Mountain View, CA, USA.
MediaEval 2016: LAPI at Predicting Media Interestingness Taskmultimediaeval
Presenter: Mihai Gabriel Constantin
LAPI at MediaEval 2016 Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Mihai G. Constantin, Bogdan Boteanu, Bogdan Ionescu
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_23.pdf
Video: https://youtu.be/4VKeMMeroG0
Abstract: This paper will present our results for the MediaEval 2016 Predicting Media Interestingness task. We proposed an approach based on video descriptors and studied several machine learning models, in order to detect the optimal configuration and combination for the descriptors and algorithms that compose our system.
MediaEval 2016 - Verifying Multimedia Use Task Overviewmultimediaeval
Presenters: Stuart E. Middleton and Christina Boididou
Verifying Multimedia Use at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Christina Boididou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, Stuart E. Middleton, Andreas Petlund, and Yiannis Kompatsiaris
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_3.pdf
Video: https://youtu.be/2Jx6OliFR-0
Abstract: This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2016 MediaEval Benchmark. The task motivates the development of automated techniques for detecting manipulated and misleading use of web multimedia content. Splicing, tampering and reposting videos and images are examples of manipulation that are part of the task definition. For the 2016 edition of the task, a corpus of images/videos and their associated posts is made available, together with labels indicating the appearance of misuse (fake) or not (real) in each case as well as some useful post metadata.
Wreck a nice beach: adventures in speech recognitionStephen Marquard
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
PPT-CCL: A Universal Phrase Tagset for Multilingual TreebanksLifeng (Aaron) Han
Many syntactic treebanks and parser toolkits are developed in the past twenty years, including dependency structure parsers and phrase structure parsers. For the phrase structure parsers, they usually utilize different phrase tagsets for different languages, which results in an inconvenience when conducting the multilingual research. This paper designs a refined universal phrase tagset that contains 9 commonly used phrase categories. Furthermore, the mapping covers 25 constituent treebanks and 21 languages. The experiments show that the universal phrase tagset can generally reduce the costs in the parsing models and even improve the parsing accuracy.
Presentation for SSW11: "Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance"
Presenter: Hieu-Thi Luong
Preprint: https://arxiv.org/abs/2106.13479
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Presenter: Dr. Xiaoxiao Miao, NII
Paper: https://arxiv.org/abs/2202.13097
Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization systems are complicated, containing an F0 extractor, speaker encoder, automatic speech recognition acoustic model (ASR AM), speech synthesis acoustic model and speech waveform generation model. Moreover, as an ASR AM is language-dependent, trained on English data, it is hard to adapt it into another language. In this paper, we propose a simpler self-supervised learning (SSL)-based method for language-independent speaker anonymization without any explicit language-dependent model, which can be easily used for other languages. Extensive experiments were conducted on the VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in Mandarin to demonstrate the effectiveness of our proposed SSL-based language-independent speaker anonymization method.
Video Retrieval for Multimedia Verification of Breaking News on Social NetworksInVID Project
This slideset presents an approach to automatically detecting breaking news events from social media streams, using event detection to collecting near real time relevant video documents from social networks regarding that breaking news. A visual analytics dashboard provides access to the results of the content processing pipeline, providing a rich interactive interface to explore emerging stories and select video material around those stories for verification.
MediaEval 2015 - JRS at Synchronization of Multi-user Event Media Taskmultimediaeval
The event synchronisation task addresses the problem of aligning media (i.e., photo and video) streams (“galleries”) from different users temporally and identifying coherent events in the streams. Our approach uses the visual similarity of image/key frame pairs based on full matching of SIFT descriptors with geometric verification. Based on the visual similarity and the given time information, a probabilistic algorithm is employed, where in each run a hypothesis is calculated for the set of time offsets with respect to the reference gallery. From the gathered hypotheses, the final set of time offsets is calculated as the medoid of all hypotheses.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2015 - CERTH at MediaEval 2015 Synchronization of Multi-User Event ...multimediaeval
This paper describes the results of our participation to the Synchronization of Multi-User Event Media Task at the MediaEval 2015 challenge. Using multiple similarity measures, we identify pairs of similar media from different galleries. We use a graph-based approach to temporally synchronize user galleries; subsequently we use time information, geolocation information and visual concept detection results to cluster all photos into different sub-events. Our method achieves good accuracy on considerably diverse datasets.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2015 - GTM-UVigo Systems for Person Discovery Task at MediaEval 2015multimediaeval
In this paper, we present the systems developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2015. The systems propose two different strategies for person discovery in audio through speaker diarization (one based on an online clustering strategy with error correction using OCR information and the other based on agglomerative hierarchical clustering) as well as intrashot and intershot trategies for face clustering.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - TUD-MMC Predicting media Interestingness Taskmultimediaeval
Presenter: Cynthia Liem
TUD-MMC at MediaEval 2016: Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cynthia Liem
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_30.pdf
Video: https://youtu.be/NQan10E_-kE
Abstract: This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media Interestingness Task. Noting that the nature of movie trailer shots is different from that of preceding tasks on image and video interestingness, we propose two baseline heuristic approaches based on the clear occurrence of people. MAP scores obtained on the development set and test set suggest that our approaches cover a limited but non-marginal subset of the interestingness spectrum. Most strikingly, our obtained scores on the Image and Video Subtasks are comparable or better than those obtained when evaluating the ground truth annotations of the Image Subtask against the Video Subtask and vice versa
MediaEval 2015 - Verifying Multimedia Use at MediaEval 2015multimediaeval
This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2015 MediaEval Benchmark. The task deals with the automatic detection of manipulation and misuse of Web multimedia content. Its aim is to lay the basis for a future generation of tools that could assist media professionals in the process of verification. Examples of manipulation include maliciously tampering with images and videos, e.g., splicing, removal/addition of elements, while other kinds of misuse include the reposting of previously captured multimedia content in a different context (e.g., a new event) claiming that it was captured there. For the 2015 edition of the task, we have generated and made available a large corpus of real-world cases of images that were distributed through tweets, along with manually assigned labels regarding their use, i.e. misleading (fake) versus appropriate (real).
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - Placing Images with Refined Language Models and Similarity S...multimediaeval
Presenter: Giorgos Kordopatis-Zilos
Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Giorgos Kordopatis-Zilos, Adrian Popescu, Symeon Papadopoulos, Yiannis Kompatsiaris
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_13.pdf
Video: https://youtu.be/WR4I3CWjcR4
Abstract: We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual and textual cues from the multimedia items, trained on datasets of different size and origin. The best results were obtained by a hybrid approach trained with external training data and using two publicly available gazetteers.
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Taskmultimediaeval
Presenter: Maigrot Cédric
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Cédric Maigrot, Vincent Claveau, Ewa Kijak, Ronan Sicre
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_45.pdf
Video: https://youtu.be/ay1zWydnijY
Abstract: This paper presents a multi-modal hoax detection system composed of text, source, and image analysis. As hoax can be very diverse, we want to analyze several modalities to better detect them. This system is applied in the context of the Verifying Multimedia Use task of MediaEval 2016. Experiments show the performance of each separated modality as well as their combination.
MediaEval 2016 - LAPI @ 2016 Retrieving Diverse Social Images Task: A Pseudo-...multimediaeval
Presenter: Bogdan Boteanu,
LAPI @ 2016 Retrieving Diverse Social Images Task: A Pseudo-Relevance Feedback Diversification Perspective In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Bogdan Boteanu, Mihai G. Constantin, Bogdan Ionescu
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_20.pdf
Video: https://youtu.be/mDI8Z31p7TY
Abstract: In this paper we present the results achieved during the 2016 MediaEval Retrieving Diverse Social Images Task, using an approach based on pseudo-relevance feedback, in which human feedback is replaced by an automatic selection of images. The proposed approach is designed to have in priority the diversification of the results, in contrast to most of the existing techniques that address only the relevance. Diversification is achieved by exploiting a hierarchical clustering scheme followed by a diversification strategy. Methods are tested on the benchmarking data and results are analyzed. Insights for future work conclude the paper.
MediaEval 2015 - Synchronization of Multi-User Event Media at MediaEval 2015:...multimediaeval
The objective of this paper is to provide an overview of the Synchronization of Multi-User Event Media (SEM) Task, which is part of the MediaEval Benchmark for Multimedia Evaluation. The SEM task was initially presented at MediaEval in 2014, with the goal of proposing a challenge in aligning multiple users’ photo galleries related to the same event but with unreliable timestamps. Besides aligning the pictures on a common timeline, participants were also required to detect the sub-events and cluster the pictures accordingly. For 2015 we have decided to extend the task also to other types of media, thus including audio and video information for a more complete and diversified representation of the analyzed event.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
MediaEval 2016 - Simula Team @ Context of Experience Taskmultimediaeval
Presenter: Konstantin Pogorelov
Simula @ MediaEval 2016 Context of Experience Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Carsten Griwodz
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_53.pdf
Video: https://youtu.be/FTIeGpHhURU
Abstract: This paper presents our approach for the Context of Multimedia Experience Task of the MediaEval 2016 Benchmark. We present different analyses of the given data using different subsets of data sources and combinations of it. Our approach gives a baseline evaluation indicating that metadata approaches work well but that also visual features can provide useful information for the given problem to solve.
Media REVEALr: A social multimedia monitoring and intelligence system for Web...Symeon Papadopoulos
Presentation of Media REVEALr, a framework for mining social and Web multimedia with the goal of supporting verification. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
The InVID Plug-in: Web Video Verification on the BrowserInVID Project
Presentation of the paper "The InVID Plug-in: Web Video
Verification on the Browser" at the 1st Int. Workshop on Multimedia Verification (MuVer) that was hosted at the ACM Multimedia Conference, October 23 - 27, 2017 Mountain View, CA, USA.
MediaEval 2016: LAPI at Predicting Media Interestingness Taskmultimediaeval
Presenter: Mihai Gabriel Constantin
LAPI at MediaEval 2016 Predicting Media Interestingness Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Mihai G. Constantin, Bogdan Boteanu, Bogdan Ionescu
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_23.pdf
Video: https://youtu.be/4VKeMMeroG0
Abstract: This paper will present our results for the MediaEval 2016 Predicting Media Interestingness task. We proposed an approach based on video descriptors and studied several machine learning models, in order to detect the optimal configuration and combination for the descriptors and algorithms that compose our system.
MediaEval 2016 - Verifying Multimedia Use Task Overviewmultimediaeval
Presenters: Stuart E. Middleton and Christina Boididou
Verifying Multimedia Use at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Christina Boididou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, Stuart E. Middleton, Andreas Petlund, and Yiannis Kompatsiaris
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_3.pdf
Video: https://youtu.be/2Jx6OliFR-0
Abstract: This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2016 MediaEval Benchmark. The task motivates the development of automated techniques for detecting manipulated and misleading use of web multimedia content. Splicing, tampering and reposting videos and images are examples of manipulation that are part of the task definition. For the 2016 edition of the task, a corpus of images/videos and their associated posts is made available, together with labels indicating the appearance of misuse (fake) or not (real) in each case as well as some useful post metadata.
Wreck a nice beach: adventures in speech recognitionStephen Marquard
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
PPT-CCL: A Universal Phrase Tagset for Multilingual TreebanksLifeng (Aaron) Han
Many syntactic treebanks and parser toolkits are developed in the past twenty years, including dependency structure parsers and phrase structure parsers. For the phrase structure parsers, they usually utilize different phrase tagsets for different languages, which results in an inconvenience when conducting the multilingual research. This paper designs a refined universal phrase tagset that contains 9 commonly used phrase categories. Furthermore, the mapping covers 25 constituent treebanks and 21 languages. The experiments show that the universal phrase tagset can generally reduce the costs in the parsing models and even improve the parsing accuracy.
Presentation for SSW11: "Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance"
Presenter: Hieu-Thi Luong
Preprint: https://arxiv.org/abs/2106.13479
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Presenter: Dr. Xiaoxiao Miao, NII
Paper: https://arxiv.org/abs/2202.13097
Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization systems are complicated, containing an F0 extractor, speaker encoder, automatic speech recognition acoustic model (ASR AM), speech synthesis acoustic model and speech waveform generation model. Moreover, as an ASR AM is language-dependent, trained on English data, it is hard to adapt it into another language. In this paper, we propose a simpler self-supervised learning (SSL)-based method for language-independent speaker anonymization without any explicit language-dependent model, which can be easily used for other languages. Extensive experiments were conducted on the VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in Mandarin to demonstrate the effectiveness of our proposed SSL-based language-independent speaker anonymization method.
Detailed presentation on various analytical tools widely used in Corpus Linguistics for corpora analysis including WORDCRUNCHER, LEXA, CWB , TACT, MICROCONCORD etc.
A Medium article on Meta A.I. Speech to Speech Textless Spoken Language Translation.
https://medium.com/@sidsanc4998/translate-your-cats-meow-884d2bdd4587
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
High-Performance and Scalable Designs of Programming Models for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will focus on challenges in designing programming models and runtime environments for Exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI+X (PGAS - OpenSHMEM/UPC/CAF/UPC++, OpenMP, and CUDA) programming models by taking into account support for multi-core systems (KNL and OpenPower), high-performance networks, GPGPUs (including GPUDirect RDMA), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries, will be presented."
Watch the video: http://wp.me/p3RLHQ-gCb
Learn more: http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Similar to MediaEval 2016 - BUT Zero-Cost Speech Recognition (20)
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
YouTube: https://youtu.be/gV-rvV3iFDA
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Presented by: Pierre-Etienne Martin
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper50.pdf
Hai Nguyen-Truong, San Cao, N. A. Khoa Nguyen, Bang-Dang Pham, Hieu Dao, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Sports Video Classification Tasks in the Multimedia Evaluation 2020 Challenge focuses on classifying different types of table tennis strokes in video segments. In this task, we - the HCMUS Team - perform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify 21 types of table tennis strokes from video segments. In total, we submit eight runs corresponding to five different models with different sets of hyper-parameters in each of our models. In addition, we apply some pre-processing techniques on the dataset in order for our model to learn and classify more accurately. According to the evaluation results, one of our team's methods out-performs the other team's. In particular, our best run achieves 31.35\% global accuracy, and all of our methods show potential results in terms of local and global accuracy for action recognition tasks.
Sports Video Classification: Classification of Strokes in Table Tennis for Me...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper2.pdf
YouTube: https://youtu.be/-bRL868b8ys
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, Jordan Calandre and Julien Morlier : Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Fine-grained action classification has raised new challenges compared to classical action classification problems. Sport video analysis is a very popular research topic, due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests, up to analysis of athletes' performances. Running since 2019 as a part of MediaEval, we offer a task which consists in classifying table tennis strokes from videos recorded in natural conditions at the University of Bordeaux. The aim is to build tools for teachers, coaches and players to analyse table tennis games. Such tools could lead to an automatic profiling of the player and adaptation of his training for improving his/her sport skills more efficiently.
Presented by: Pierre-Etienne Martin
Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper61.pdf
YouTube: https://youtu.be/brmI4g3jLS4
Ricardo Kleinlein, Cristina Luna-Jiménez, Fernando Fernández-Martínez and Zoraida Callejas : Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper reports on the GTH-UPM team experience in the Predicting Media Memorability task at MediaEval 2020. Teams were requested to predict memorability scores at both short-term and long-term, understanding such score as a measure of whether a video was perdurable in a viewer's memory or not. Our proposed system relies on a late fusion of the scores predicted by three sequential models, each trained over a different modality: video captions, aural embeddings and visual optical flow-based vectors. Whereas single-modality models show a low or zero Spearman correlation coefficient value, their combination considerably boosts performance over development data up to 0.2 in the short-term memorability prediction subtask and 0.19 in the long-term subtask. However, performance over test data drops to 0.016 and -0.041, respectively.
Presented by: Ricardo Kleinlein
Essex-NLIP at MediaEval Predicting Media Memorability 2020 Taskmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper52.pdf
Janadhip Jacutprakart, Rukiye Savran Kiziltepe, John Q. Gan, Giorgos Papanastasiou and Alba G. Seco de Herrera : Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we present the methods of approach and the main results from the Essex NLIP Team’s participation in the MediEval 2020 Predicting Media Memorability task. The task requires participants to build systems that can predict short-term and long-term memorability scores on real-world video samples provided. The focus of our approach is on the use of colour-based visual features as well as the use of the video annotation meta-data. In addition, hyper-parameter tuning was explored. Besides the simplicity of the methodology, our approach achieves competitive results. We investigated the use of different visual features. We assessed the performance of memorability scores through various regression models where Random Forest regression is our final model, to predict the memorability of videos.
Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper6.pdf
YouTube: https://youtu.be/ySGGu_4vaxs
Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu and Alan F. Smeaton : Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable? Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video to Text dataset, containing more action rich video content as compare with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for the run submission.
Presented by: Rukiye Savran Kiziltepe
Fooling an Automatic Image Quality Estimatormultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper45.pdf
Benoit Bonnet, Teddy Furon and Patrick Bas : Fooling an Automatic Image Quality Estimator. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper we present our work on the 2020 MediaEval task: Pixel "Privacy: Quality Camouflage for Social Images". Blind Image Quality Assessment (BIQA) is a classifier that for any given image will return a quality score. Our task is to modify an image to decrease its BIQA score while maintaining a good perceived quality. Since BIQA is a deep neural network, we worked on an adversarial attack approach of the problem.
Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper16.pdf
YouTube: https://youtu.be/ix_b9K7j72w
Zhengyu Zhao : Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents the submission of our RU-DS team to the Pixel Privacy Task 2020. We propose to fool the blind image quality assessment model by transforming images based on optimizing a human-understandable color filter. In contrast to the common work that relies on small, $L_p$-bounded additive pixel perturbations, our approach yields large yet smooth perturbations. Experimental results demonstrate that in the specific context of this task, our approach is able to achieve strong adversarial effects, but has to sacrifice the image appeal.
Presented by: Zhengyu Zhao
Pixel Privacy: Quality Camouflage for Social Imagesmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper77.pdf
YouTube: https://youtu.be/8Rr4KknGSac
Zhuoran Liu, Zhengyu Zhao, Martha Larson and Laurent Amsaleg : Pixel Privacy: Quality Camouflage for Social Images. Proc. of MediaEval 2020, 14-15 December 2020, Online.
High-quality social images shared online can be misappropriated for unauthorized goals, where the quality filtering step is commonly carried out by automatic Blind Image Quality Assessment (BIQA) algorithms. Pixel Privacy benchmarks privacy-protective approaches that protect privacy-sensitive images against unethical computer vision algorithms. In the 2020 task, participants are encouraged to develop camouflage methods that can effectively decrease the BIQA quality score of high-quality images and maintain image appeal. The camouflaged images need to be either imperceptible to the human eye, or it can be a visible enhancement.
Presented by: Zhuoran Liu
HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matchingmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper73.pdf
YouTube: https://youtu.be/TadJ6y7xZeA
Thuc Nguyen-Quang, Tuan-Duy Nguyen, Thang-Long Nguyen-Ho, Anh-Kiet Duong, Xuan-Nhat Hoang, Vinh-Thuyen Nguyen-Truong, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Matching text and images based on their semantics has an important role in cross-media retrieval. However, text and images in articles have a complex connection. In the context of MediaEval 2020 Challenge, we propose three multi-modal methods for mapping text and images of news articles to the shared space in order to perform efficient cross-retrieval. Our methods show systemic improvement and validate our hypotheses, while the best-performed method reaches a recall@100 score of 0.2064.
Presented by: Thuc Nguyen-Quang
Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper72.pdf
Sabarinathan D and Suganya Ramamoorthy : Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attention Unit. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Colorectal cancer is the third most common cause of cancer worldwide. In the era of medical Industry, identifying colorectal cancer in its early stages has been a challenging problem. Inspired by these issues, the main objective of this paper is to develop a Multi supervision net algorithm for segmenting polys on a comprehensive dataset. The risk of colorectal cancer could be reduced by early diagnosis of poly during a colonoscopy. The disease and their symptoms are highly varying and always a need for a continuous update of knowledge for the doctors and medical analyst. The diseases fall into different categories and a small variation of symptoms may lead to higher rate of risk. We have taken Medico polyp challenge dataset, which consists of 1000 segmented polyp images from gastrointestinal track. We proposed an efficient Net B4 as a pre-trained architecture in multi-supervision net. The model is trained with multiple output layers. We present quantitative results on colorectal dataset to evaluate the performance and achieved good results in all the performance metrics. The experimental results proved that the proposed model is robust and provides a good level of accuracy in segmenting polyps on a comprehensive dataset for different metrics such as Dice coefficient, Recall, Precision and F2.
HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ ...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper47.pdf
YouTube: https://youtu.be/vMsM4zg2-JY
Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams' methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).
Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper31.pdf
Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed Unaiz Haider, Talha Ahmed, Zeshan Khan and Muhammad Atif Tahir : Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Identification of polyps in endoscopic images is critical for the diagnosis of colon cancer. Finding the exact shape and size of polyps requires the segmentation of endoscopic images. This research explores the advantage of using depth-wise separable convolution in the atrous convolution of the ResUNet++ architecture. Deep atrous spatial pyramid pooling was also implemented on the ResUNet++ architecture. The results show that architecture with separable convolution has a smaller size and fewer GFLOPs without degrading the performance too much.
Deep Conditional Adversarial learning for polyp Segmentationmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper22.pdf
Debapriya Banik and Debotosh Bhattacharjee : Deep Conditional Adversarial learning for polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This approach has addressed the Medico automatic polyp segmentation challenge which is a part of Mediaeval 2020. We have proposed a deep conditional adversarial learning based network for the automatic polyp segmentation task. The network comprises of two interdependent models namely a generator and a discriminator. The generator network is a FCN employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a comparative result on the test dataset provided by the organizers of the challenge.
A Temporal-Spatial Attention Model for Medical Image Detectionmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper21.pdf
Hwang Maxwell, Wu Cai, Hwang Kao-Shing, Xu Yong Si and Wu Chien-Hsing : A Temporal-Spatial Attention Model for Medical Image Detection. Proc. of MediaEval 2020, 14-15 December 2020, Online.
A local region model with attentive temporal-spatial pathways is proposed for automatically learning various target structures. The attentive spatial pathway highlights the salient region to generate bounding boxes and ignores irrelevant regions in an input image. The proposed attention mechanism allows efficient object localization and the overall predictive performance is increased because there are fewer false positives for the object detection task for medical images with manual annotations. The experimental results show that proposed models consistently increase the base architectures' predictive performance for different datasets and training sizes without undue computational efficiency.
HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper20.pdf
YouTube: https://youtu.be/CVelQl5Luf0
Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.
Fine-tuning for Polyp Segmentation with Attentionmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper15.pdf
Rabindra Khadka : Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes how the transfer of prior knowledge can effectively take on segmentation tasks with the help of attention mechanisms. The UNet model pretrained on brain MRI dataset was fine-tuned with the polyp dataset. Attention mechanism was integrated to focus on relevant regions in the input images. The implemented architecture is evaluated on 200 validation images based on intersection over union and dice score between groundtruth and predicted region. The model demonstrates a promising result with computational efciency.
Bigger Networks are not Always Better: Deep Convolutional Neural Networks for...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper12.pdf
Adrian Krenzer and Frank Puppe : Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents our team's (AI-JMU) approach to the Medico automated polyp segmentation challenge. We consider deep convolutional neural networks to be well suited for this task. To determine the best architecture we test and compare state of the art backbones and two different heads. Finally we achieve a Jaccard index of 73.74\% on the challenge test set. We further demonstrate that bigger networks do not always perform better. However the growing network size always increases the computational complexity.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper40.pdf
YouTube: https://youtu.be/SL5Hvu1mARY
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
insect taxonomy importance systematics and classification
MediaEval 2016 - BUT Zero-Cost Speech Recognition
1. fit
speech fit
BUT Zero-Cost 2016 Speech Recognition
Miroslav Skácel, Martin Karafiát, Lucas Ondel, Albert Uchytil, Igor Szöke
BUT Speech@FIT
Brno University of Technology
Czech Republic
MediaEval 2016 Workshop, October 20-21, 2016, Hilversum, Netherlands
2. Overview
Our ideas for this task were:
• to use previous knowledge of low-resource languages
• to use existing systems from Babel1
• to adapt that on Vietnamese
• to follow zero-cost requirements
We ended up with:
• lots of data processing and cleaning
• 2x LVCSR sub-task systems
• 1x subword sub-task systems
• suggestions for further improvements
1https://www.iarpa.gov/index.php/research-programs/babel
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3. Data preparation
• audio downsampled from 16kHz to 8kHz
• got Vietnamese alphabet from wordlist
• cleaned other characters (punctuation marks, brackets, etc.)
• numerals expanded to textual form
2
4. Data segmentation
• audio longer than 1 minute caused troubles
• alignment from first training stages used
• data split when:
• silence longer than 0.5s occurs
• segment would be longer than 15s
3
5. Language models
3 LMs were created:
1) cleaned transcriptions from train set
2) cleaned subtitles
3) cleaned text from websites
• 3 or more words
• we combined them together
4
6. LVCSR systems - GMM/DNN2
2Martin Karafiát et al. BUT Neural Network Features for Spontaneous Vietnamese in
BABEL. In Proceedings of ICASSP 2014, pages 5659–5663. IEEE Signal Processing Society,
2014.
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7. LVCSR systems - BLSTM3
• 16kHz audio, original transcriptions
• Kaldi/TNet toolkits
3Martin Karafiát et al. Multilingual BLSTM and Speaker-Specific Vector Adaptation in
2016 BUT Babel System. Accepted at SLT 2016, 2016.
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8. Subword system
• Acoustic Unit Discovery4
(AUD) model
• unlabeled data - no transcription needed
• like phone-loop model, fully Bayesian, Dirichlet distribution
• no fixed number of components like in HMM
• can learn complexity of model
4Lucas Ondel et al. Variational Inference for Acoustic Unit Discovery. In Procedia
Computer Science, volume 2016, pages 80–86. Elsevier Science, 2016.
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10. Further work
Ideas for future experiments:
• find better way to clean original transcriptions
• methods to detect inappropriate audio/transcriptions
• adding noise to audio for more robust system
• audio reverberation using RIR
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