ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding

arXiv:2609.29193 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/YusungChoi/ImCorr

Terminology

• Quantization — 22× in this paper · explained in 12 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Percentage of Correct Keypoints — 1× in this paper
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes

Sources

• Adam: A Method for Stochastic Optimization
• Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence
• SPair-71k: A Large-scale Benchmark for Semantic Correspondence
• DINOv2: Learning Robust Visual Features without Supervision
• AP-10K: A Benchmark for Animal Pose Estimation in the Wild

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