mlsanityData to 14 Oct 2026

Motion-Based Weak Supervision for Video Parsing with Application to Colonoscopy

arXiv 2210.10594 ↗First posted 16 Oct 2022cs.CV cs.LGcited by 3

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Abstract (arXiv)

We propose a two-stage unsupervised approach for parsing videos into phases. We use motion cues to divide the video into coarse segments. Noisy segment labels are then used to weakly supervise an appearance-based classifier. We show the effectiveness of the method for phase detection in colonoscopy videos.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 16 Oct 2022.

Builds on

3 of 7 references.

References · 3

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentationcited by 373
  3. 2016Fast Optical Flow using Dense Inverse Searchcited by 71

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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networkscited by 3
  2. 2023Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Timecited by 0
  3. 2023Semi-supervised Quality Evaluation of Colonoscopy Procedurescited by 0