RGB-Infrared person re-IDentification (re-ID) aims to match RGB and infrared (IR) images of the same person. However, the modality discrepancy between RGB and IR images poses a significant challenge for re-ID. To address this issue, this paper proposes a Proxy-based Embedding Alignment (PEA) method to align the RGB and IR modalities in the embedding space. PEA introduces modality-specific identity proxies and leverages the sample-to-proxy relations to learn the model. Specifically, PEA focuses on three types of alignments: intra-modality alignment, inter-modality alignment, and cycle alignment. Intra-modality alignment aims to align sample features and proxies of the same identity within a modality. Inter-modality alignment aims to align sample features and proxies of the same identity across different modalities. Cycle alignment requires that a proxy is aligned with itself after tracing it along a cross-modality cycle (e.g., IR→RGB→IR). By integrating these alignments into the training process, PEA effectively mitigates the impact of modality discrepancy and learns discriminative features across modalities. We conduct extensive experiments on several RGB-IR re-ID datasets, and the results show that PEA outperforms current state-of-the-art methods. Notably, on SYSU-MM01 dataset, PEA achieves 71.0% mAP under the multi-shot setting of the indoor-search protocol, surpassing the best-performing method by 7.2%.

In person re-IDentification (re-ID) task, the learning of part-level features benefits from fine-grained information. To facilitate part alignment, which is a prerequisite for learning part-level features, a popular approach is to detect semantic parts with the use of human parsing or pose estimation. Such methods of semantic partition do offer cues to good part alignment but are prone to noisy part detection, especially when they are employed in an off-the-shelf manner. In response, this paper proposes a novel part feature learning method for re-ID, that suppresses the impact of noisy semantic part detection through Supervised Non-local Similarity (SNS) learning. Given several detected semantic parts, SNS first locates their center points on the convolutional feature maps for use as a set of anchors and then evaluates the similarity values between these anchors and each pixel on the feature maps. The non-local similarity learning is supervised such that: each anchor should be similar to itself and simultaneously dissimilar to any other anchors, thus yielding the SNS. Finally, each anchor absorbs features from all of the similar pixels on the convolutional feature maps to generate a corresponding part feature (SNS feature). We evaluate our method with extensive experiments conducted under both holistic and partial re-ID scenarios. Experimental results confirm that SNS consistently improves re-ID accuracy using human parsing or pose estimation, and that our results are on par with state-of-the-art methods.