With the rapid development of intelligent surveillance technology, the massive amount of multimodal data (e.g., videos, images, and text) has imposed higher demands on efficient information retrieval and security. Traditional single-modal retrieval methods struggle to meet practical requirements, making multimodal image-text retrieval a research hotspot in this field. Existing approaches, however, still face challenges in fine-grained semantic alignment and suffer from rigid matching mechanisms. To address these issues, this paper introduces SeaNcr, a novel framework that integrates cross-modal semantic entity alignment with non-correspondence reasoning. Our method constructs class-level entity representations enhanced by saliency-guided masking to capture discriminative semantic features. A pseudo-frozen asynchronous optimization strategy is introduced to maintain semantic consistency across modalities by associating stable entity representations with dynamically updated encoder features. Moreover, to overcome rigid matching, we design a non-correspondence reasoning module that jointly leverages intra-modal similarity and cross-modal mutual nearest neighbor constraints, optimizing matching flexibility and generalization. Extensive experiments validate that SeaNcr significantly enhances cross-modal feature representation and retrieval robustness, achieving state-of-the-art performance on multiple person re-identification benchmarks.

Person re-identification (re-ID) is a research hotspot in the field of intelligent monitoring and security. Domain generalizable (DG) person re-identification transfers the trained model directly to the unseen target domain for testing, which is closer to the practical application than supervised or unsupervised person re-ID. Meta-learning strategy is an effective way to solve the DG problem, nevertheless, existing meta-learning-based DG re-ID methods mainly simulates the test process in a single aspect such as identity or style, while ignoring the completely different person identities and styles in the unseen target domain. As to this problem, we consider a double disentangling from two levels of training strategy and feature learning, and propose a novel dualistic disentangled meta-learning (D$^{\mathbf {2}}$ML) model. D$^{\mathbf {2}}$ML is composed of two disentangling stages, one is for learning strategy, which spreads one-stage meta-test into two-stage, including an identity meta-test stage and a style meta-test stage. The other is for feature representation, which decouples the shallow layer features into identity-related features and style-related features. Specifically, we first conduct identity meta-test stage on different person identities of the images, and then employ a feature-level style perturbation module (SPM) based on Fourier spectrum transformation to conduct the style meta-test stage on the image with diversified styles. With these two stages, abundant changes in the unseen domain can be simulated during the meta-test phase. Besides, to learn more identity-related features, a feature disentangling module (FDM) is inserted at each stage of meta-learning and a disentangled triplet loss is developed. Through constraining the relationship between identity-related features and style-related features, the generalization ability of the model can be further improved. Experimental results on four public datasets show that our D$^{\mathbf {2}}$ML model achieves superior generalization performance compared to the state-of-the-art methods.

Person re-identification (re-ID) aims to match images of the same person in different camera views. The general convolutional neural network has the problem of insufficient ability to discriminate specific targets, resulting in limited feature representation when directly applied in person re-ID task. In this paper, a person re-ID baseline model based on attention block neural architecture search is proposed. Since the attention mechanism is helpful to improve the feature expression ability of the network, attention blocks are automatically searched and added to ResNet-50 model, which aims to find the most suitable model structure for person re-ID task. Besides, in order to integrate information between the samples of same identity, an intra-class self-distillation loss is introduced according to the idea of knowledge integration. Experiments on two popular datasets confirm the effectiveness of our baseline. The code has been released in https://github.com/Nicholasxin/Attention-NASReID.

Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present, most UCD person re-ID methods perform "supervised learning" by assigning pseudo labels to the target domain, which leads to poor re-ID performance due to the pseudo label noise. To address this problem, a multi-loss optimization learning (MLOL) model is proposed for UCD person re-ID. In addition to using the information of clustering pseudo labels from the perspective of supervised learning, two losses are designed from the view of similarity exploration and adversarial learning to optimize the model. Specifically, in order to alleviate the erroneous guidance brought by the clustering error to the model, a ranking-average-based triplet loss learning and a neighbor-consistency-based loss learning are developed. Combining these losses to optimize the model results in a deep exploration of the intra-domain relation within the target domain. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that our model outperforms the state-of-the-art UCD re-ID methods with a clear advantage.