MediaEval 2016 - Multimodal Person Discovery in TV Broadcast
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Presenter: Hervé Bredin Multimodal Person Discovery in Broadcast TV at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Hervé Bredin, Claude Barras, Camille Guinaudeau Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_4.pdf Video: https://youtu.be/OOfOtjsmXV4 Abstract: We describe the “Multimodal Person Discovery in Broadcast TV” task of MediaEval 2016 benchmarking initiative. Participants are asked to return the names of people who can be both seen as well as heard in every shot of a collection of videos. The list of people is not known a priori and their names has to be discovered in an unsupervised way from media content using text overlay or speech transcripts for the primary runs. The task is evaluated using information retrieval metrics, based on a posteriori collaborative annotation of the test corpus.
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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...
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
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MediaEval 2016 - Emotional Impact of Movies Task
Presenter: Emmanuel Dellandréa
The MediaEval 2016 Emotional Impact of Movies Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Emmanuel Dellandréa, Liming Chen, Yoann Baveye, Mats Sjöberg, Christel Chamaret
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_6.pdf
Video: https://youtu.be/c2itaauMVmQ
Abstract: This paper provides a description of the MediaEval 2016 ”Emotional Impact of Movies” task. It continues builds on previous years’ editions of the Affect in Multimedia Task: Violent Scenes Detection. However, in this year’s task, participants are expected to create systems that automatically predict the emotional impact that video content will have on viewers, in terms of valence and arousal scores. Here we provide insights on the use case, task challenges, dataset and ground truth, task run requirements and evaluation metrics.
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 - UNED-UV @ Retrieving Diverse Social Images Task
Ana Garcia Serrano
UNED-UV @ Retrieving Diverse Social Images Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Angel C. González, Xaro B. Garcia, Ana García-Serrano, Esther de Ves Cuenca
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_17.pdf
Video: https://youtu.be/EyuN7IA1HFk
Abstract: This paper details the participation of the UNED-UV group at the MediaEval 2016 Retrieving Diverse Social Images Task using a multimodal approach. Several Local Logistic Regression models, which use the visual low-level features, estimate the relevance probability for all the images in the dataset. Then, the images are ranked by selecting the highest probability image at each of the textual clusters. These textual clusters are generated by making use of a textual algorithm based on Formal Concept Analysis (FCA) and Hierarchical Agglomerative Clustering (HAC) to detect the latent topics addressed. The images will be then diversified according to detected topics.
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: A Multimodal System for the Verifying Multimedia Use Task
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 - Placing Task Overview
Presenter: Jaeyoung Choi
The Placing Task at MediaEval 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Jaeyoung Choi, Claudia Hauff, Olivier Van Laere, Bart Thomee
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_7.pdf
Video: https://youtu.be/8646sUGqMbg
Abstract: The seventh edition of the Placing Task at MediaEval focuses on two challenges: (1) estimation-based placing, which addresses estimating the geographic location where a photo or video was taken, and (2) verification-based placing, which addresses verifying whether a photo or video was indeed taken at a pre-specified geographic location. Like the previous edition, we made the organizer baselines for both subtasks available as open source code, and published a live leaderboard that allows the participants to gain insights into the effectiveness of their approaches compared to the official baselines and in relation to each other at an early stage, before the actual run submissions are due.
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 - 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 - 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.
MediaEval 2016: LAPI at Predicting Media Interestingness Task
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 2015 - CERTH at MediaEval 2015 Synchronization of Multi-User Event ...
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
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MediaEval 2016 - TUD-MMC Predicting media Interestingness Task
MediaEval 2016 - UNED-UV @ Retrieving Diverse Social Images Task
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MediaEval 2016 - UVigo System for Multimodal Person Discovery in Broadcast TV...
MediaEval 2016 - UVigo System for Multimodal Person Discovery in Broadcast TV...
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task
MediaEval 2016: A Multimodal System for the Verifying Multimedia Use Task
MediaEval 2016 - Retrieving Diverse Social Images Task Overview
MediaEval 2016 - Retrieving Diverse Social Images Task Overview
MediaEval 2016 - EUMSSI Team at the MediaEval Person Discovery Challenge
MediaEval 2016 - EUMSSI Team at the MediaEval Person Discovery Challenge
MediaEval 2016 - Tag Propagation in Talking Face Graphs
MediaEval 2016 - Tag Propagation in Talking Face Graphs
MediaEval 2016: LAPI at Predicting Media Interestingness Task
MediaEval 2016: LAPI at Predicting Media Interestingness Task
MediaEval 2015 - CERTH at MediaEval 2015 Synchronization of Multi-User Event ...
MediaEval 2015 - CERTH at MediaEval 2015 Synchronization of Multi-User Event ...
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Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
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Paper: http://ceur-ws.org/Vol-2882/paper6.pdf
YouTube: https://youtu.be/ySGGu_4vaxs
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Paper: http://ceur-ws.org/Vol-2882/paper45.pdf
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YouTube: https://youtu.be/ix_b9K7j72w
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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.
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YouTube: https://youtu.be/vMsM4zg2-JY
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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).
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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.
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A Temporal-Spatial Attention Model for Medical Image Detection
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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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原版纸张【微信：741003700 】【(uvic毕业证书)维多利亚大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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MediaEval 2016 - Multimodal Person Discovery in TV Broadcast
- 1. Multimodal Person Discovery in TV Broadcast Hervé Bredin / Camille Guinaudeau / Claude Barras
- 2. Motivation • Huge TV archives are useless if not searchable • People ❤ people • Need for person-based indexes 2
- 3. Task A BA Hello Mrs B Mr A blah blah shot #1 shot #2 shot #3 A B B blah blah shot #4 spe ev A B blah blah A tex tr INPUT OUTPUT L 3 Tag each shot with the name of people who can be both seen as well as heard in the shot.
- 4. Task (2) A BA Hello Mrs B Mr A blah blah shot #1 shot #2 shot #3 A B B blah blah shot #4 spe ev A B blah blah A tex tr INPUT OUTPUT L 4 The list of people is not known a priori. The use of prior biometric models is not allowed.
- 5. Task (3) A BA Hello Mrs B Mr A blah blah shot #1 shot #2 shot #3 A B B blah blah shot #4 spe ev A B blah blah A tex tr INPUT OUTPUT L 5 Names must be discovered from text overlay or speech transcripts.
- 6. Baseline 6 Lowering the technological barriers to entry…
- 7. Baseline (2) • "audio" baseline speaker diarization + propagation of OCR names • "video" baseline face clustering + propagation of OCR names • "audio-visual" baseline temporal intersection of both 7
- 8. Datasets INA French France 2 France 5 90 hours 63k shots DW German / English Euromaxx DW News 50 hours 28k shots 3-24 Catalan 3-24 13 hours 3k shots 8
- 9. Evaluation • Submissions shot_id firstname_lastname confidence • Queries firstname_lastname • Sort shots by edit distance to the query confidence scores • Mean average precision @ 10 9
- 10. Practical aspects 10 Submission webpage Collaborative annotation Live leaderboard More technical details tomorrow… 1 primary run 4 contrastive runs as simple as possible 1/5 of test set updated daily
- 12. Results 12 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 EU M SSI M O TIF U PC Visual baseline AVbaseline G TM -U Vigo TokyoTech H C M U S Audio baseline MAP @ 10 | 599 queries 🏆
- 13. Results 13 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 EU M SSI M O TIF U PC Visual baseline AVbaseline G TM -U Vigo TokyoTech H C M U S Audio baseline MAP @ 10 | 599 queries | 156 queries (2+ videos)
- 14. Tested approaches • {speech turn | face} clustering followed by cluster tagging • {speech turn | face} tagging followed by "supervised" recognition • fusion at {decision | score} level • joint multimodal clustering and tagging with graph-based tag propagation • talking-face detection • nobody used speech transcripts 14 🙁
- 15. Tested approaches • {speech turn | face} clustering followed by cluster tagging • {speech turn | face} tagging followed by "supervised" recognition • fusion at {decision | score} level • joint multimodal clustering and tagging with graph-based tag propagation • talking-face detection • nobody used speech transcripts 14 🙁 #1 #1 #1
- 16. Tested approaches • {speech turn | face} clustering followed by cluster tagging • {speech turn | face} tagging followed by "supervised" recognition • fusion at {decision | score} level • joint multimodal clustering and tagging with graph-based tag propagation • talking-face detection • nobody used speech transcripts 14 🙁 #1 #1 #1 #2
- 17. Tested approaches • {speech turn | face} clustering followed by cluster tagging • {speech turn | face} tagging followed by "supervised" recognition • fusion at {decision | score} level • joint multimodal clustering and tagging with graph-based tag propagation • talking-face detection • nobody used speech transcripts 14 🙁 #1 #1 #1 #2 #3
- 18. Collaborative annotations 15 0 2000 4000 6000 8000 MOTIF organizers EUMSSI UPC 20k annotations / 3.5k positive shots 🏆
- 19. Acknowledgments