Predicting the future from first person (egocentric) vision: A survey
TLDR
It is highlighted that methods for future prediction from egocentric vision can have a significant impact in a range of applications and that further research efforts should be devoted to the standardisation of tasks and the proposal of datasets considering real-world scenarios such as the ones with an industrial vocation.About:
This article is published in Computer Vision and Image Understanding.The article was published on 2021-10-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Augmented reality.read more
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Ego4D: Around the World in 3,000 Hours of Egocentric Video
Kristen Grauman,Andrew Westbury,Eugene Byrne,Zachary Chavis,Antonino Furnari,Rohit Girdhar,Jackson Hamburger,Hao Jiang,Miao Liu,Xingyu Liu,Miguel Martin,Tushar Nagarajan,Ilija Radosavovic,Santhosh K. Ramakrishnan,Fiona Ryan,Jayant Sharma,Michael Wray,Mengmeng Xu,Eric Zhongcong Xu,Chen Zhao,Siddhant Bansal,Dhruv Batra,Vincent Cartillier,Sean Crane,Tien Do,Morrie Doulaty,Akshay Erapalli,Christoph Feichtenhofer,Adriano Fragomeni,Qichen Fu,Christian Fuegen,Abrham Gebreselasie,Cristina González,James Hillis,Xuhua Huang,Yifei Huang,Wenqi Jia,Weslie Khoo,Jachym Kolar,Satwik Kottur,Anurag Kumar,Federico Landini,Chao Li,Yanghao Li,Zhenqiang Li,Karttikeya Mangalam,Raghava Modhugu,Jonathan Munro,Tullie Murrell,Takumi Nishiyasu,Will Price,Paola Ruiz Puentes,Merey Ramazanova,Leda Sari,Kiran Somasundaram,Audrey Southerland,Yusuke Sugano,Ruijie Tao,Minh Vo,Yuchen Wang,Xindi Wu,Takuma Yagi,Yunyi Zhu,Pablo Arbeláez,David J. Crandall,Dima Damen,Giovanni Maria Farinella,Bernard Ghanem,Vamsi K. Ithapu,C. V. Jawahar,Hanbyul Joo,Kris M. Kitani,Haizhou Li,Richard Newcombe,Aude Oliva,Hyun Soo Park,James M. Rehg,Yoichi Sato,Jianbo Shi,Mike Zheng Shou,Antonio Torralba,Lorenzo Torresani,Mingfei Yan,Jitendra Malik +83 more
TL;DR: The Ego4D dataset as mentioned in this paper was used for de-identification of videos by some of the universities, such as the University of Bristol and the National University of Singapore.
Proceedings ArticleDOI
Ego4D: Around the World in 3,000 Hours of Egocentric Video
TL;DR: The Ego4D dataset as discussed by the authors provides 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries.
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Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset.
TL;DR: The study extensively analyses the performance of recent visual trackers and baseline FPV trackers with respect to different aspects and considering a new performance measure, and shows that object tracking in FPV is challenging.
Journal ArticleDOI
Visual Object Tracking in First Person Vision
TL;DR: In this paper , the authors present the first systematic investigation of single object tracking in First Person Vision (FPV) and extensively analyze the performance of 42 algorithms including generic object trackers and baseline FPV-specific trackers.
Book ChapterDOI
Untrimmed Action Anticipation
TL;DR: In this paper , the authors propose an untrimmed action anticipation task, which, similarly to temporal action detection, requires predictions to be made before the actions actually take place, and compare results on the EPIC-KITCHENS-100 dataset.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.