Text-based person re-identification (ReID) has enabled canonical applications in searching for and tracking targets from large-scale surveillance images with textual descriptions. Yet, existing text-based person ReID systems employ centralized model training that gathers images captured by different institutes' cameras into one place, which poses severe privacy threats to sensitive institutional information. This work is then devoted to exploring privacy-preserving text-based person ReID and proposes the framework of FedSH by tailoring the federated learning paradigm for distributed searching knowledge extraction. Specifically, FedSH resolves the local model generalization and entity boundary obscuring limitations, caused by inner-institute data homogeneity and inter-institute data heterogeneity, via building multi-granularity feature representation and a semantically self-aligned network. Meanwhile, it reduces the communication burden introduced by the embedding for multiple modals by updating common representation subspaces during federated learning. Experimental results on two public benchmarks demonstrate that our method can achieve at most 16.47% and 16.02% person ReID performance improvement by the Rank-1 metric, compared with 6 State-of-The-Art (SoTA) baselines and 6 ablation studies. We believe that our work will inspire the community to investigate the potential of implementing Federated Learning in real-world image retrieval and ReID scenarios.

Cross-modal retrieval aims to enable a flexible bi-directional retrieval experience across different modalities ( e.g ., searching for videos with texts). Many existing efforts tend to learn a common semantic representation embedding space in which items of different modalities can be directly compared, wherein the positive global representations of video-text pairs are pulled close while the negative ones are pushed apart via pair-wise ranking loss. However, such a vanilla loss would unfortunately yield ambiguous feature embeddings for texts of different videos, causing inaccurate cross-modal matching and unreliable retrievals. Toward this end, we propose a multimodal contrastive knowledge distillation method for instance video-text retrieval, called MCKD, by adaptively using the general knowledge of self-supervised model (teacher) to calibrate mixed boundaries. Specifically, the teacher model is tailored for robust (less-ambiguous) visual-text joint semantic space by maximizing mutual information of co-occurred modalities during multimodal contrastive learning. This robust and structural inter-instance knowledge is then distilled, with the help of explicit discrimination loss, to a student model for improved matching performance. Extensive experiments on four public benchmark video-text datasets (MSR-VTT, TGIF, VATEX, and Youtube2Text) demonstrate that our MCKD can achieve at most 8.8%, 6.4%, 5.9%, and 5.3% improvement in text-to-video performance by the R@1 metric, compared with 14 SoTA baselines.

Text-based Person Re-identification (T-ReID), which aims at retrieving a specific pedestrian image from a collection of images via text-based information, has received significant attention. However, previous research has overlooked a challenging yet practical form of T-ReID: dealing with image galleries mixed with occluded and inconsistent personal visuals, instead of ideal visuals with a full-body and clear view. Its major challenges lay in the insufficiency of benchmark datasets and the enlarged semantic gap incurred by arbitrary occlusions and modality gap between text description and visual representation of the target person. To alleviate these issues, we first design an Occlusion Generator (OGor) for the automatic generation of artificial occluded images from generic surveillance images. Then, a fine-granularity token selection mechanism is proposed to minimize the negative impact of occlusion for robust feature learning, and a novel multi-granularity contrastive consistency alignment framework is designed to leverage intra-/inter-granularity of visual-text representations for semantic alignment of occluded visuals and query texts. Experimental results demonstrate that our method exhibits superior performance. We believe this work could inspire the community to investigate more dedicated designs for implementing T-ReID in real-world scenarios. The source code is available at https://github.com/littlexinyi/MGCC.