mlsanityData to 7 Oct 2026

An Evaluation of Hybrid Annotation Workflows on High-Ambiguity Spatiotemporal Video Footage

arXiv 2510.21798 ↗First posted 20 Oct 2025cs.CV cs.HC cs.LGcited by 1

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Abstract (arXiv)

Manual annotation remains the gold standard for high-quality, dense temporal video datasets, yet it is inherently time-consuming. Vision-language models can aid human annotators and expedite this process. We report on the impact of automatic Pre-Annotations from a tuned encoder on a Human-in-the-Loop labeling workflow for video footage. Quantitative analysis in a study of a single-iteration test involving 18 volunteers demonstrates that our workflow reduced annotation time by 35% for the majority (72%) of the participants. Beyond efficiency, we provide a rigorous framework for benchmarking AI-assisted workflows that quantifies trade-offs between algorithmic speed and the integrity of human verification.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 42 references that are in the map. First posted on arXiv 20 Oct 2025.

Builds on

32 of 42 references.

References · 32

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20 more
  1. 2022X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrievalcited by 279
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footagecited by 2

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