title: Local All-Pair Correspondence for Point Tracking
summary: We introduce LocoTrack, a highly accurate and efficient model designed for
  the task of tracking any point (TAP) across video sequences. Previous
  approaches in this task often rely on local 2D correlation maps to establish
  correspondences from a point in the query image to a local region in the target
  image, which often struggle with homogeneous regions or repetitive features,
  leading to matching ambiguities. LocoTrack overcomes this challenge with a
  novel approach that utilizes all-pair correspondences across regions, i.e.,
  local 4D correlation, to establish precise correspondences, with bidirectional
  correspondence and matching smoothness significantly enhancing robustness
  against ambiguities. We also incorporate a lightweight correlation encoder to
  enhance computational efficiency, and a compact Transformer architecture to
  integrate long-term temporal information. LocoTrack achieves unmatched accuracy
  on all TAP-Vid benchmarks and operates at a speed almost 6 times faster than
  the current state-of-the-art.
paper_authors._id: 669f3f5a3e173b3293dfafc0
paper_authors.name: Seokju Cho
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paper_authors.fullname: Seokju Cho
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paper_authors.name: Jiahui Huang
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paper_authors.fullname: Jiahui Huang
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paper_authors.user: heiwang1997
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paper_authors.name: Jisu Nam
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paper_authors._id: 669f3f5a3e173b3293dfafc3
paper_authors.name: Honggyu An
paper_authors._id: 669f3f5a3e173b3293dfafc4
paper_authors.name: Seungryong Kim
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paper_authors.fullname: Seungryong Kim
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paper_authors.user: seungryong
paper_authors._id: 669f3f5a3e173b3293dfafc5
paper_authors.name: Joon-Young Lee
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
paper_authors.fullname: Joon-Young Lee
paper_authors.type: user
paper_authors.user: joonyounglee
paper_title: Local All-Pair Correspondence for Point Tracking
paper_summary: We introduce LocoTrack, a highly accurate and efficient model designed for
  the task of tracking any point (TAP) across video sequences. Previous
  approaches in this task often rely on local 2D correlation maps to establish
  correspondences from a point in the query image to a local region in the target
  image, which often struggle with homogeneous regions or repetitive features,
  leading to matching ambiguities. LocoTrack overcomes this challenge with a
  novel approach that utilizes all-pair correspondences across regions, i.e.,
  local 4D correlation, to establish precise correspondences, with bidirectional
  correspondence and matching smoothness significantly enhancing robustness
  against ambiguities. We also incorporate a lightweight correlation encoder to
  enhance computational efficiency, and a compact Transformer architecture to
  integrate long-term temporal information. LocoTrack achieves unmatched accuracy
  on all TAP-Vid benchmarks and operates at a speed almost 6 times faster than
  the current state-of-the-art.
paper_ai_summary: LocoTrack enhances video point tracking using 4D correlation and a compact Transformer to achieve high accuracy and speed.
paper_ai_keywords: local 4D correlation bidirectional correspondence correlation encoder Transformer architecture TAP-Vid benchmarks