Papers › Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by "hyperpixels" that leverage a small number of relevant features selected among early to late layers of a convolutional neural network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel flow, sets a new state of the art on three standard benchmarks as well as a new dataset, SPair-71k, which contains a significantly larger number of image pairs than existing datasets, with more accurate and richer annotations for in-depth analysis.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic correspondence | Caltech-101 | HPF | IoU | 63 | #1 of 2 | Archive leaderboard | report |
| Semantic correspondence | Caltech-101 | HPF | LT-ACC | 87 | #1 of 2 | Archive leaderboard | report |
| Semantic correspondence | PF-PASCAL | HPF | PCK | 88.3 | #13 of 15 | Archive leaderboard | report |
| Semantic correspondence | PF-WILLOW | HPF | PCK | 76.3 | #8 of 8 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | HPF | PCK | 28.2 | #22 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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