Abstract

Cross-view re-identification of Hanwoo cattle remains challenging: weak texture provides only limited identity cues, and large pose and viewpoint changes easily distort these cues, causing severe feature dispersion. As a result, samples from the same individual may be scattered in the feature space, whereas visually similar individuals can form ambiguous local neighborhoods. To address this problem, we propose a framework that explicitly improves identity compactness for weakly textured cattle ReID. Specifically, Pose- and Text-Conditioned Inpainting Diffusion (PTID) is trained to generate pose-diverse yet identity-consistent samples and, during inference, aggregates their features with the original input feature to approximate a compact identity center and reduce pose-induced feature dispersion. Dual-Adaptive Viewpoint-Aware Feature Centralization (DVFC) further refines retrieval features by adaptively exploiting viewpoint consistency and neighborhood reliability, thereby suppressing unreliable neighbor mixing under large cross-view variation. We also construct a Hanwoo ReID dataset containing 37 identities and 12,480 images collected from three farm environments with substantial variations in pose, illumination, occlusion, and background conditions. Extensive experiments on multiple strong baselines under both closed-set and open-set settings demonstrate consistent improvements, with gains of up to 40.8 points in mAP and 26.1 points in Rank-1 accuracy. These findings demonstrate that explicitly improving identity compactness through identity-consistent generation and viewpoint-aware feature centralization is a promising solution for the robust re-identification of weakly textured cattle.

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