mlsanityData to 7 Oct 2026

VADER: Video Alignment Differencing and Retrieval

arXiv 2303.13193 ↗First posted 23 Mar 2023cs.CVcited by 6

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Abstract (arXiv)

We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely aligns partial video fragments to candidate videos using a robust visual descriptor and scalable search over adaptively chunked video content. A transformer-based alignment module then refines the temporal localization of the query fragment within the matched video. A space-time comparator module identifies regions of manipulation between aligned content, invariant to any changes due to any residual temporal misalignments or artifacts arising from non-editorial changes of the content. Robustly matching video to a trusted source enables conclusions to be drawn on video provenance, enabling informed trust decisions on content encountered.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 62 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

33 of 62 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generationcited by 5
  2. 2024Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasetscited by 1
  3. 2025Counteracting temporal attacks in Video Copy Detectioncited by 1
  4. 2025Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Contentcited by 0
  5. 2026Efficient Logic Gate Networks for Video Copy Detectioncited by 0
  6. 2026MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matchingcited by 0