MediaEval 2016 - ETH-CVL: Textual-Visual Embeddings and Video2GIF for Video Interestingness
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Michael Gygli ETH-CVL @ MediaEval 2016: Textual-Visual Embeddings and Video2GIF for Video Interestingness In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Arun B. Vasudevan, Michael Gygli, Anna Volokitin, Luc Van Gool Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_24.pdf Video: https://youtu.be/8qe-NIPSD-4 Abstract: This paper presents the methods that underly our submission to the Predicting Media Interestingness Task at MediaEval 2016. Our contribution relies on two main approaches: (i) A similarity metric between image and text and (ii) a generic video highlight detector. In particular, we develop a method for learning the similarity of text and images, by projecting them into the same embedding space. This embedding allows to find video frames that are both, canonical and relevant w.r.t the title of the video. We present the result of different configurations and give insights into when our best performing method works well and where it has difficulties.
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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 - Simula Team @ Context of Experience Task
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.
MediaEval 2016 - ININ Submission to Zero Cost ASR Task
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 - IR Evaluation: Putting the User Back in the Loop
This document discusses different approaches to evaluating information retrieval (IR) systems, including user studies, in-situ evaluation, A/B testing, interleaving, and collection-based evaluation using test collections like Cranfield. It describes the TREC Session Track, which aimed to improve search over an entire user session, and the TREC Tasks Track, which focuses on understanding and assisting users with their underlying tasks. The CLEF Dynamic Search for Complex Tasks task also examines simulating users to dynamically build test collections and understand good document rankings and sessions for complex tasks.
MediaEval 2016 - UPMC at MediaEval2016 Retrieving Diverse Social Images Task
Presenter: Sabrina Tollari
UPMC at MediaEval 2016 Retrieving Diverse Social Images Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Sabrina Tollari
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_14.pdf
Video: https://youtu.be/_uletoYIZGQ
Abstract: In the MediaEval 2016 Retrieving Diverse Social Images Task, we proposed a general framework based on agglomerative hierarchical clustering (AHC). We tested the provided credibility descriptors as a vector input for our AHC. The results on devset showed that this vector based on the credibility descriptors is the best feature, but unfortunately that is not confirmed on testset. To merge several features, we chose to merge feature similarities. Tests on devset showed that to merge similarities using linear or weighted-max operators gave, most of the time, better results than using only one feature. This results is partially confirmed on testset.
MediaEval 2016 - UNIFESP Predicting Media Interestingness Task
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 - Emotion in Music Task: Lessons Learned
This document summarizes lessons learned from three years of the Emotion in Music Task, which aimed to recognize emotions expressed in music over time. It describes the tasks, datasets, and evaluation metrics used each year. The quality of emotion annotations improved over time, but issues remained around absolute scales, reaction time, and scaling of emotional expression. The best performing systems used bi-directional long short-term memory neural networks. Possible solutions discussed include changing the task focus or data collection interface to address annotation problems.
MediaEval 2016 - TUD-MMC Predicting media Interestingness Task
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 2016 - Tag Propagation in Talking Face Graphs
Presenter: Guillaume Gravier
PUCMinas and IRISA at Multimodal Person Discovery In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Gabriel Sargent, Gabriel Barbosa de Fonseca, Izabela Lyon Freire, Ronan Sicre, Zenilton K. G. Patrocínio Jr, Silvio Jamil F. Guimarães, and Guillaume Gravier
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_34.pdf
Video: https://youtu.be/O2Ecgy89hN4
Abstract: This paper describes the systems developed by PUC Minas and IRISA for the person discovery task at MediaEval 2016. We adopt a graph-based representation and investigate two tag-propagation approaches to associate overlays cooccurring with some speaking faces to other visually or audio-visually similar speaking faces. Given a video, we first build a graph from the detected speaking faces (nodes) and their audio-visual similarities (edges). Each node is associated to its co-occurring overlays (tags) when they exist. Then, we consider two tagpropagation approaches, respectively based on a random walk strategy and on Kruskal’s algorithm.
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The document discusses the OpenMIC Challenge 2017, which aims to establish a sustainable model for music information retrieval (MIR) evaluation. It proposes using openly licensed music, distributed computation of system outputs, and incremental annotation of the most informative examples to reduce costs and bias compared to previous evaluation models like MIREX. Key aspects include collecting Creative Commons music, defining classification and retrieval tasks, developing a backend to manage annotations, and releasing an initial development set for participants to train systems on.
MediaEval 2016 - UVigo System for Multimodal Person Discovery in Broadcast TV...
Presenter: Paula López Otero
GTM-UVigo System for Multimodal Person Discovery in Broadcast TV Task at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Paula L. Otero, Laura Docio-Fernandez, Carmen G. Mateo
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_36.pdf
Video: https://youtu.be/KrSjh47rEdc
Abstract: In this paper, we present the system developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2016. The proposed approach consists in a novel strategy for person discovery which is not based on speaker and face diarisation as in previous works. In this system, the task is approached as a person recognition problem: there is an enrolment stage, where the voice and face of each discovered person are detected and, for each shot, the most suitable voice and face are assigned using the i-vector paradigm. These two biometric modalities are combined by decision fusion.
MediaEval 2016 - EUMSSI Team at the MediaEval Person Discovery Challenge
Presenter: Nam Le
EUMSSI Team at the MediaEval Person Discovery Challenge 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Nam D. Le, Sylvain Meignier, Jean-Marc Odobez
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_46.pdf
Video: https://youtu.be/axt_PxhIrJ4
Abstract: We present the results of the EUMSSI team’s participation in the Multimodal Person Discovery task. The goal is to identify all people who simultaneously appear and speak in a video corpus. In the proposed system, besides improving each modality, we emphasize on the ranking of multiple results from both audio stream and visual stream.
MediaEval 2016 - RECOD at Placing Task
Samuel G. Fadel, RECOD, Brazil
RECOD @ Placing Task of MediaEval 2016: A Ranking Fusion Approach for Geographic-Location Prediction of Multimedia Objects In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Javier A. V. Muñoz, Lin Tzy Li, Ícaro C. Dourado, Keiller Nogueira, Samuel G. Fadel, Otávio A. B. Penatti1, Jurandy Almeida, Luís A. M. Pereira, Rodrigo T. Calumby, Jefersson A. dos Santos, Ricardo da S. Torres
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_22.pdf
Video: https://youtu.be/njZHg-5GR1I
Abstract: We describe the approach proposed by the RECOD team for the estimation-based sub-task of Placing Task at MediaEval 2016. Our approach uses genetic programming (GP) to combine ranked lists defined in terms of textual and visual descriptors to automatically assign geographic locations to images and videos.
MediaEval 2016 - Retrieving Diverse Social Images Task Overview
Presenter: Maia Zaharieva
Retrieving Diverse Social Images at MediaEval 2013: Objectives, Dataset and Evaluation Working Notes Proceedings of the MediaEval 2013 Workshop, Barcelona, Spain, October 18-19, CEUR-WS.org, ISSN 1613-0073 (2013) by Bogdan Ionescu, María Menéndez, Henning Müller, Adrian Popescu
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_5.pdf
Video: https://youtu.be/1prt8LSTLf8
Abstract: This paper provides an overview of the Retrieving Diverse Social Images task that is organized as part of the MediaEval 2016 Benchmarking Initiative for Multimedia Evaluation. The task addresses the problem of result diversification in the context of social photo retrieval where images, metadata, text information, user tagging profiles and content and text models are available for processing. We present the task challenges, the proposed data set and ground truth, the required participant runs and the evaluation metrics.
MediaEval 2016 - the C@merata task: Natural Language Queries Derived from Exa...
Presenters: Richard Sutcliffe and Cynthia Liem
The C@merata task at MediaEval 2016: Natural Language Queries Derived from Exam Papers, Articles and Other Sources against Classical Music Scores in MusicXML In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Richard Sutcliffe, Tom Collins, Eduard Hovy, Richard Lewis, Chris Fox, and Deane Root
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_55.pdf
Video: https://youtu.be/dL6ouVaM5eo
Abstract: Cl@ssical Music Extraction of Relevant Aspects by Text Analysis
(C@merata) is a shared evaluation held at MediaEval and this is the third time the task has run. The input is a natural language query (‘F# in the cello’) and the output is a passage in a MusicXML score which contains this note played on the instrument in question. There are 200 such questions each year and evaluation is via modified versions of Precision, Recall and FMeasure. In 2014 and 2015 the best Beat F (BF) scores were 0.797 and 0.620, both attained by CLAS. This year, queries were more difficult and in addition the most experienced groups from previous years were unable to take part. In consequence, the best BF was 0.070. This year, there was progress concerning the development of the queries, many of these being derived from real sources such as exam papers, books and scholarly articles. We are thus converging on our goal of relating musical references in complex natural language texts to passages in music scores.
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Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...
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 Estimator
This document discusses fooling an automatic image quality estimator (BIQA) using adversarial attacks. It presents adapting the definition of an adversarial attack since BIQA predicts a quality score rather than class. It also covers challenges in generating adversarial samples as integer images and quantizing samples before JPEG compression to simulate artifacts. Results show success rates for different quality targets and selection rates by evaluators.
Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...
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 Images
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-Matching
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...
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++ ...
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...
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 Segmentation
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 Detection
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...
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 Attention
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...
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...
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 ...
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.
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- 1. ETH-CVL @ MediaEval 2016: Textual-Visual Embeddings and Video2GIF for Video Interestingness Michael Gygli, PhD student @ Computer Vision Lab, ETH Zurich Work with Arun Balajee Vasudevan, Anna Volokitin and Luc Van Gool 1
- 2. Content ▪ Overview ▪ Textual-visual embedding ▪ Video2GIF ▪ Results 2Michael Gygli, PhD student @ ETH Zurich06/21/2016
- 3. Finding the most interesting and relevant content ▪ Two dominant approaches ▪ Use of textual information (title, category) to obtain a video specific model, often using web image priors E.g. [Khosla et al. CVPR 13], [Liu et al. IJCAI 09], Song et al. CVPR 15] ▪ Supervised learning with generic features and large training set E.g. [Potapov et al. ECCV 2014], [Sun et al. ECCV 2014], [Gygli et al. CVPR 16] ▪ Some methods use both E.g. [Liu et al. CVPR 15] 3
- 5. Multi-task deep visual-semantic embedding for video thumbnail selection [Liu et al. CVPR 15] ▪ Use Bing image search data (query, image, # of clicks) to learn a joint embedding space for images and text ▪ Compute frame relevance as cosine similarity between the query or title embedding and the frame embedding [Liu et al. CVPR 2015] 5Michael Gygli, PhD student @ ETH Zurich06/21/2016
- 6. Our contribution with improvements over Liu et al. ● Siamese network ● Text Embedding Model ○ Words encoded through word2vec ○ LSTM to obtain fixed length embedding ● Convolutional Neural Network model ○ Fine-tuned VGG-19 ● Training Data based on learning a ranking of: (query+, image+, image- (“cat”, , ) 6
- 7. Video2GIF
- 8. Video2GIF: Automatic Generation of Animated GIFs from Video [Gygli et al. CVPR 16] Approach ▪ Work with segments as units ▪ Obtained through change-point detection [Song et al. CVPR 15] ▪ Train a deep neural network for ranking segments ... 8 Example video Highest scoring Lowest scoring Michael Gygli, PhD student @ ETH Zurich06/21/2016
- 9. Video2GIF: Automatic Generation of Animated GIFs from Video Approach ▪ Train a deep neural network for ranking segments ▪ Built on C3D network [Tran et al. ICCV 2015] ▪ Objective: score positives higher than negatives h: scoring function s+ : positive segment s- : negative segment 9Michael Gygli, PhD student @ ETH Zurich06/21/2016
- 10. Video2GIF: Dataset 10Michael Gygli, PhD student @ ETH Zurich06/21/2016 Available on github.com/gyglim/video2gif_dataset ▪ Large-scale training data: GIFs created from YouTube videos ▪ Align GIF back to video ▪ This part defines a positive example ▪ Assume non-selected parts are less interesting than selected part
- 11. Results Task Run mAP Image 1 0.1866 2 0.1952 3 0.1858 Video 1 0.1362 2 0.1574 Frame-based: • Run 1: Visual-semantic embedding trained on Clickture dataset • Run 2: As Run 1, with fine-tuning on development set • Run 3: As Run 1, but trained on a larger subset of Clickture Segment-based: • Run 1: Video2GIF • Run 2: Averaged score of Visual-semantic embedding and Video2GIF
- 12. Qualitative Results - Run 2 Title: Captives Title: After Earth Predicted best frame True best frame Predicted best frame True best frame
- 13. More information ▪ Paper Video2GIF: Automatic Generation of Animated GIFs from Video. M. Gygli, Y. Song, L. Cao, CVPR 2016 ▪ Slides on Video Summarization as Subset Selection @ Tutorial on Optimization Algorithms for Subset Selection and Summarization in Large Data Sets, CVPR 2016 https://t.co/mQIpxMab3v ▪ Demo website for Video2GIF: http://video2gif.info/autogif ▪ Paper Multi-task deep visual-semantic embedding for video thumbnail selection. W. Liu, T. Mei, Y. Zhang, C. Che, and J. Luo, CVPR 2015
- 14. Questions?