mlsanityData to 14 Oct 2026

Correlation-aware active learning for surgery video segmentation

arXiv 2311.08811 ↗First posted 15 Nov 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

Semantic segmentation is a complex task that relies heavily on large amounts of annotated image data. However, annotating such data can be time-consuming and resource-intensive, especially in the medical domain. Active Learning (AL) is a popular approach that can help to reduce this burden by iteratively selecting images for annotation to improve the model performance. In the case of video data, it is important to consider the model uncertainty and the temporal nature of the sequences when selecting images for annotation. This work proposes a novel AL strategy for surgery video segmentation, COWAL, COrrelation-aWare Active Learning. Our approach involves projecting images into a latent space that has been fine-tuned using contrastive learning and then selecting a fixed number of representative images from local clusters of video frames. We demonstrate the effectiveness of this approach on two video datasets of surgical instruments and three real-world video datasets. The datasets and code will be made publicly available upon receiving necessary approvals.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 40 of its 55 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

40 of 55 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025SASVi -- Segment Any Surgical Videocited by 4
  2. 2024Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentationcited by 2
  3. 2026CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotationcited by 0
  4. 2026Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Trackingcited by 0