Research map / Probabilistic and scientific modeling / Molecules and proteins
Dense Flow from Adaptive Correspondence
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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes dense optical flow that adapts the matching unit (correspondence primitives) rather than keeping motion fixed at grid points, paired with evidence matching; full text unavailable so details are limited to the abstract.
Problem
Dense optical flow must be precise and robust, but existing methods fix the matching unit to dense grid points while improving only the evidence used for matching.
Approach
Argues the unit of matching should be adaptive rather than fixed to a dense pixel grid, coupling adaptive correspondence units with the matching evidence for dense flow estimation.
Limitations
- Only the abstract is available; experimental details unknown
Closest papers by meaning
- UFM: A Simple Path towards Unified Dense Correspondence with Flow (NeurIPS 2025 · similarity 0.90)
- FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking (CVPR 2024 · similarity 0.88)
- Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations (CVPR 2025 · similarity 0.87)
- FlowFeat: Pixel-Dense Embedding of Motion Profiles (NeurIPS 2025 · similarity 0.87)
- Adapting Dense Matching for Homography Estimation with Grid-based Acceleration (CVPR 2025 · similarity 0.87)
- Dense Optical Tracking: Connecting the Dots (CVPR 2024 · similarity 0.87)
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