Hierarchical disturbance and Group Inference for video-based visible-infrared person re-identification

Abstract

Video-based Visible-Infrared person Re-identification (VVI-ReID) is challenging due to the large inter-view and inter-modal discrepancies. To alleviate these discrepancies, most existing works only focus on whole images, while more id-related partial information is ignored. Furthermore, the inference decision is commonly based on the similarity of two samples. However, the semantic gap between the query and gallery samples inevitably exists due to their inter-view misalignment, no matter whether the modality-gap is removed. In this paper, we proposed a Hierarchical Disturbance (HD) and Group Inference (GI) method to handle aforementioned issues. Specifically, the HD module models the inter-view and inter-modal discrepancies as multiple image styles, and conducts feature disturbances through partially transferring body styles. By hierarchically taking the partial and global features into account, our model is capable of adaptively achieving invariant but identity-related features. Additionally, instead of establishing similarity between the query sample and each gallery sample independently, the GI module is further introduced to extract complementary information from all potential intra-class gallery samples of the given query sample, which boosts the performance on matching hard samples. Extensive experiments substantiate the superiority of our method compared with state-of-the arts.