Papers › Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur, Eric Chen, Varun Jampani, Deqing Sun, Ming-Hsuan Yang
While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset, for both pre-training validating models. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset, outperforming the state of the art by 5.5p and 11.0p absolute gains, respectively. Our code and datasets are publicly available at: https://telling-left-from-right.github.io/.
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Code
Syntology Ran 11 of 15 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 9 ran with no contract checked.
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Code Syntology ran Syntology
15 samples harvested; 11 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 | PF-PASCAL | GeoAware-SC (Supervised, AP-10K P.T.) | PCK | 95.7 | #2 of 15 | Archive leaderboard | report |
| Semantic correspondence | PF-PASCAL | GeoAware-SC (Supervised) | PCK | 95.1 | #3 of 15 | Archive leaderboard | report |
| Semantic correspondence | PF-PASCAL | GeoAware-SC (Zero-Shot) | PCK | 82.6 | #14 of 15 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | GeoAware-SC (Supervised, AP-10K P.T.) | PCK | 85.6 | #1 of 22 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | GeoAware-SC (Supervised) | PCK | 82.9 | #3 of 22 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | GeoAware-SC (Zero-Shot) | PCK | 68.5 | #6 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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