Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos

By Tahmida Binte Mahmud, Mahmudul Hasan, Amit K. Roy-Chowdhury. 2017

90 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Time-Series & Forecasting

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Citations in the corpus

Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, neutral): “[149] Tahmida Mahmud, Mahmudul Hasan, and Amit K RoyChowdhury.”
Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video (2020, methodology): “…of the encoder-decoder framework to encode past observations and produce hypotheses of future actions [8], and the use of object speciﬁc features [56] to determine which objects are present in the scene, we show that other approaches, such as the direct regression of future representations [8],…”
Temporal Aggregate Representations for Long-Range Video Understanding (2020, background): “[24] presents a model for predicting both the next action and its starting position.”
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction (2019, background): “In the computer vision community there is a large body of works dedicated to video and action prediction [20, 19, 24, 21, 7, 5, 17].”
Predicting the Future from First Person (Egocentric) Vision: A Survey (2021, background): “Some works [11, 54] perform experiments on datasets of instructional activities [55, 56] which often resemble egocentric videos due to the framing focusing on the hands.”
Part-Activated Deep Reinforcement Learning for Action Prediction (2018, background): “In spite of the enormous amount of works conducted in this area [21, 25, 26], this task is still challenging due to the fundamental challenges inherent in the problem like the large variance among the activities and huge spatiotemporal scale variation.”
When will you do what? - Anticipating Temporal Occurrences of Activities (2018, background): “In the recent work [15], the starting time of the future activity is estimated as well.”
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention (2019, methodology): “Previous works investigated different forms of action and activity anticipation from third person video [1, 15, 18, 25, 27, 28, 31, 32, 33, 40, 58, 64].”
Leveraging Uncertainty to Rethink Loss Functions and Evaluation Measures for Egocentric Action Anticipation (2018, background): “[23] investigated the prediction of labels and starting times of future activities.”