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Technical Report: Temporal Aggregate Representations

arXiv 2106.03152 ↗First posted 6 Jun 2021cs.CVcited by 8

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Abstract (arXiv)

This technical report extends our work presented in [9] with more experiments. In [9], we tackle long-term video understanding, which requires reasoning from current and past or future observations and raises several fundamental questions. How should temporal or sequential relationships be modelled? What temporal extent of information and context needs to be processed? At what temporal scale should they be derived? [9] addresses these questions with a flexible multi-granular temporal aggregation framework. In this report, we conduct further experiments with this framework on different tasks and a new dataset, EPIC-KITCHENS-100.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 6 Jun 2021.

Builds on

8 of 12 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Non-local Neural Networkscited by 2,293
  3. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 902
  4. 2018Scaling Egocentric Vision: The EPIC-KITCHENS Datasetcited by 795
  5. 2020Understanding Human Hands in Contact at Internet Scalecited by 281
  6. 2019What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attentioncited by 105
  7. 2018Timeception for Complex Action Recognitioncited by 89
  8. 2020PIC: Permutation Invariant Convolution for Recognizing Long-range Activitiescited by 8

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 4.

8 papers.