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FlexGlue: Sparse-to-Dense Feature Matching and Visual Disambiguation

Paper recorded by Signals 4 on 2026-10-23 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.CV · 计算机视觉 · first seen 2026-10-26

Abstract

To estimate correspondences across images, existing approaches match either sparse interest points, which is highly efficient but limited by repeatability, or dense pixel grids, which is robust but expensive. Furthermore, these approaches are trained with only matching pairs and thus often fail to reject unrelated pairs that exhibit visual symmetries, which is equally important in applications lik

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#159 most recent of 756 cs.CV papers we have recorded · ↑ newer: UniAction: Unified Action Conditioning for World Models · ↓ older: MiD-VLM: Detecting and Grounding Global Semantic Misinformation in Ima
Cite this page: FlexGlue: Sparse-to-Dense Feature Matching and Visual Disambiguation: the #159 most recent of 756 cs.CV papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/flexglue-sparse-to-dense-feature-matching-and-visual-disambiguation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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