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When people observe events, they are able to abstract key information and build concise summaries of what is happening.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Ucf101: A dataset of 101 human actions classes from videos in the wild
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A thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching
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Adam: A method for stochastic optimization
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Microsoft coco: Common objects in context
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Coherent multi-sentence video description with variable level of detail
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Learning deep features for scene recognition using places database
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Activitynet: A large-scale video benchmark for human activity understanding
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Maximum-margin structured learning with deep networks for 3d human pose estimation
Sijin Li, Weichen Zhang, and Antoni B. Chan · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
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Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Apostol (Paul) Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
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Unsupervised learning from narrated instruction videos
Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, and Simon Lacoste-Julien · 2016
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Soundnet: Learning sound representations from unlabeled video
Yusuf Aytar, Carl Vondrick, and Antonio Torralba · 2016
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Unsupervised learning of spoken language with visual context
David Harwath, Antonio Torralba, and James Glass · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ambient sound provides supervision for visual learning
Andrew Owens, Jiajun Wu, Josh McDermott, William Freeman, and Antonio Torralba · 2016
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Recognizing fine-grained and composite activities using hand-centric features and script data
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Hollywood in homes: Crowdsourcing data collection for activity understanding
Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta · 2016
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Msr-vtt: A large video description dataset for bridging video and language
Jun Xu, Tao Mei, Ting Yao, and Yong Rui · 2016
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Colorful image colorization
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Every moment counts: Dense detailed labeling of actions in complex videos
Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori, and Li Fei-Fei · 2018
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Multi-label image classification with regional latent semantic dependencies
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The sound of pixels
Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, and Antonio Torralba · 2018
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Weakly-supervised video object grounding from text by loss weighting and object interaction
Luowei Zhou, Nathan Louis, and Jason J Corso · 2018
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Towards automatic learning of procedures from web instructional videos
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Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Joao Carreira and Andrew Zisserman · 2017
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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VSE++: Improving visual-semantic embeddings with hard negatives
Fartash Faghri, David J Fleet, Jamie Ryan Kiros, and Sanja Fidler · 2017
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The “something something” video database for learning and evaluating visual common sense
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Luowei Zhou, Chenliang Xu, and Jason J. Corso · 2018
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Grounding spoken words in unlabeled video
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Large-scale representation learning from visually grounded untranscribed speech
Gabriel Ilharco, Yuan Zhang, and Jason Baldridge · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Tsm: Temporal shift module for efficient video understanding
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Adaptiveface: Adaptive margin and sampling for face recognition
Hao Liu, Xiangyu Zhu, Zhen Lei, and Stan Z. Li · 2019
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Howto100m: Learning a text-video embedding by watching hundred million narrated video clips
Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic · 2019
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Moments in time dataset: one million videos for event understanding
Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva · 2019
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Multi-moments in time: Learning and interpreting models for multi-action video understanding
Mathew Monfort, Kandan Ramakrishnan, Alex Andonian, Barry A McNamara, Alex Lascelles, Bowen Pan, Quanfu Fan, Dan Gutfreund, Rogerio Feris, and Aude Oliva · 2019
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wav2vec: Unsupervised pre-training for speech recognition
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli · 2019
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Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid · 2019
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Learning words by drawing images
Didac Suris, Adria Recasens, David Bau, David Harwath, James Glass, and Antonio Torralba · 2019
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Vatex: A large-scale, high-quality multilingual dataset for video-and-language research
Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Dima Damen, Hazel Doughty, Giovanni Maria Farinella, , Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2020
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Jointly discovering visual objects and spoken words from raw sensory input
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Boosting few-shot learning with adaptive margin loss
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Avlnet: Learning audio-visual language representations from instructional videos
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