# ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding

arXiv: https://arxiv.org/abs/2609.29193 · HTML: https://mlsanity.com/paper/2609.29193/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 19 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence](https://mlsanity.com/paper/2305.14334/) (2023)
- [SPair-71k: A Large-scale Benchmark for Semantic Correspondence](https://mlsanity.com/paper/1908.10543/) (2019)
- [AP-10K: A Benchmark for Animal Pose Estimation in the Wild](https://mlsanity.com/paper/2108.12617/) (2021)
- [Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence](https://mlsanity.com/paper/2311.17034/) (2023)
- [Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence](https://mlsanity.com/paper/2210.02689/) (2022)
- [Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels](https://mlsanity.com/paper/2506.05312/) (2025)
- [Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence](https://mlsanity.com/paper/2506.08220/) (2025)
- [GECO: Geometrically Consistent Embedding with Lightspeed Inference](https://mlsanity.com/paper/2508.00746/) (2025)
- [MARCO: Navigating the Unseen Space of Semantic Correspondence](https://mlsanity.com/paper/2604.18267/) (2026)

## Abstract (arXiv)

The strong performance that modern semantic correspondence methods achieve at standard thresholds plateaus sharply at fine-grained thresholds. We argue that this plateau stems not from the representational capacity of backbone features, but from a grid-tied readout. Patch-based vision transformers tokenize images onto discrete grids, introducing two forms of quantization error: querying nearest patch features instead of exact keypoints on the source side, and the absence of grid features representing precise ground-truth locations on the target side. We quantify this quantization ceiling across all 499,188 keypoints in SPair-71k: under the standard 448x448, patch-14 setting, 84.9% of ground-truth keypoints have no grid feature representing their precise location at PCK@0.01. This is a structural limitation at the representation level, independent of the matching strategy. We address this with ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding, which formulates correspondence estimation over a continuous feature field queryable at arbitrary continuous coordinates. A FiLM-conditioned decoder is trained to embed sub-pixel positional information into the feature field. Querying the field directly at exact keypoint coordinates theoretically eliminates representation-level quantization error on the source side, while decoding onto a grid denser than the backbone grid substantially reduces quantization error on the target side. On SPair-71k and AP-10K (intra-species, cross-species, and cross-family), ImCorr improves performance at fine-grained thresholds (PCK@0.01-0.05), achieving a 6.2 percentage point gain over the prior state of the art at PCK@0.01 on SPair-71k. These results demonstrate that representational continuity is an effective solution for precise semantic correspondence. Code is available at https://github.com/YusungChoi/ImCorr.