Revisiting Semantic Correspondence: When Feature Aggregation Hurts Structural Integrity.

Semantic correspondence seeks to establish matches between different instances of the same category. A common paradigm for this task leverages high-quality features from stable diffusion (SD) and DINOv2. However, we identify a widely overlooked yet critical issue: common feature aggregation disrupts the structural integrity of SD features, degrading semantic matching performance. We revisit and analyze this phenomenon, and propose structure-aware aggregation (SAA) for SD features as a direct replacement for common feature aggregation methods. SAA uses filtering to decompose SD features into fine texture details and coarse contour structures. It aggregates only the texture components while preserving the contours. This divide-and-conquer mechanism enables SAA to significantly enhance the performance of state-of-the-art semantic correspondence models without increasing trainable parameters or computational overhead. Extensive qualitative and quantitative experiments confirm our analysis and validate the effectiveness of SAA. Moreover, SAA generalizes well across geometric, cross-species, and cross-family semantic correspondence tasks. Code is available at https://github.com/wzhlearning/SAA.