Temporal Perception and Prediction in Ego-Centric Video

By Yipin Zhou, Tamara L. Berg. 2015

72 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Generating Videos with Scene Dynamics (2016, background): “Our work is also related to efforts to predict the future in video [33, 22, 44, 51, 42, 17, 8, 55] as well as concurrent work in future generation [6, 15, 20, 50, 56, 43].”
Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, background): “The task is similar to action recognition (e.g., [100,110,139,221,243]) because in some cases a specific action can correspond to a specific state change.”
From Recognition to Cognition: Visual Commonsense Reasoning (2018, neutral): “Commonsense Reasoning Our task unifies work involving reasoning about commonsense phenomena, such as physics [54, 84], social interactions [2, 77, 12, 27], procedure understanding [91, 3] and predicting what might happen next in a video [74, 17, 92, 78, 18, 64, 85].”
Learning Temporal Transformations from Time-Lapse Videos (2016, methodology): “In the ego-centric activity domain, [14] encodes the prediction problem as a binary task of selecting which of two video clips is ﬁrst in temporal ordering.”
Unsupervised Learning using Sequential Verification for Action Recognition (2016, background): “Several recent papers [52, 62, 64] use egomotion constraints from video to further constrain the learning.”
Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video (2020, neutral): “Zhou and Berg [58] studied methods to infer the ordering of egocentric video video Fig.”
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification (2016, background): “[48, 49] also consider the task of predicting the future from videos, but consider it as their end task and do not use it for unsupervised pre-training.”
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video (2017, background): “Previously, three works noted the challenge and difﬁ-culty in deﬁning temporal bounds for egocentric videos [29, 1, 37].”
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition (2019, background): “As a result, there has been a renewed interest from the computer vision community on collecting large-scale datasets [8, 35] as well as developing new or adapting existing methods to the firstperson point-of-view scenario [9, 17, 21, 32, 44, 46].”
Ego-Topo: Environment Affordances From Egocentric Video (2020, background): “Recent action anticipation [13, 75, 15, 5, 53, 16, 61] predicts the immediate next action (e.”
What will Happen Next? Forecasting Player Moves in Sports Videos (2017, background): “Predicting pedestrian trajectories [15, 17, 13, 14, 29] and anticipating future human activities [14, 16, 35, 45, 19, 10] has seen considerable interest over the past few years.”
Next-active-object prediction from egocentric videos (2017, background): “[22] concentrated on the task of inferring temporal ordering from egocentric videos.”
Who Let the Dogs Out? Modeling Dog Behavior from Visual Data (2018, background): “[55] infer the temporal ordering of two snippets of ego-centric videos and predict what will happen next.”
Deep Sequential Context Networks for Action Prediction (2017, background): “By comparison, [35, 21, 14] predict what will happen in the future, and [9, 19] localizes the starting and ending frames of an incomplete event.”
Deep Multimodal Feature Encoding for Video Ordering (2020, methodology): “It is also worth noting that temporal ordering has been used for other applications such as predicting what happens next in egocentric videos [70], using the steadiness of visual change in videos to penalize higher order differences [21], or to recognize complex, long-term activities [26].”
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos (2021, background): “It is the subject of a recent surge in research interest in first-person activity recognition [35, 77], anticipation [21, 1], and video summarization [36, 74, 13] with many valuable future applications in augmented reality and robotics.”
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition (2018, background): “Other self-supervised learning tasks on videos use cues like ego-motion [63, 22, 1] as a supervisory signal and other modalities beyond raw pixels such as audio [37, 36] and robot motion [2, 40, 41, 42].”
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention (2019, background): “Anticipation in First Person Vision Past works on anticipation from egocentric visual data have investigated different problems and considered different evaluation frameworks [5, 10, 17, 42, 45, 47, 50, 53, 65, 68].”
Leveraging Uncertainty to Rethink Loss Functions and Evaluation Measures for Egocentric Action Anticipation (2018, background): “[39] studied computational approaches to recover the correct order of egocentric video segments.”
Zero-Shot Anticipation for Instructional Activities (2018, background): “We are the ﬁrst to tackle such a problem in this form; prior works in complex activity recognition are severely limited in the number of categories and steps [6, 29, 30, 43], while works in action anticipation rely on strong supervision [5, 31, 61].”