mlsanityData to 14 Oct 2026

Unsupervised Action Segmentation for Instructional Videos

arXiv 2106.03738 ↗First posted 7 Jun 2021cs.CVcited by 4

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Abstract (arXiv)

In this paper we address the problem of automatically discovering atomic actions in unsupervised manner from instructional videos, which are rarely annotated with atomic actions. We present an unsupervised approach to learn atomic actions of structured human tasks from a variety of instructional videos based on a sequential stochastic autoregressive model for temporal segmentation of videos. This learns to represent and discover the sequential relationship between different atomic actions of the task, and which provides automatic and unsupervised self-labeling.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

11 of 16 references.

References · 11

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  2. 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 950
  3. 2015Unsupervised Learning from Narrated Instruction Videoscited by 116
  4. 2019Unsupervised learning of action classes with continuous temporal embeddingcited by 76
  5. 2016Connectionist Temporal Modeling for Weakly Supervised Action Labelingcited by 70
  6. 2017Weakly Supervised Action Learning with RNN based Fine-to-coarse Modelingcited by 70
  7. 2018NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learningcited by 67
  8. 2018Unsupervised Learning and Segmentation of Complex Activities from Videocited by 61
  9. 2016Weakly supervised learning of actions from transcriptscited by 46
  10. 2019AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architecturescited by 46
  11. 2020Set-Constrained Viterbi for Set-Supervised Action Segmentationcited by 35

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Citing papers per year: 2021: 1; 2022: 0; 2023: 3.

4 papers.