Unsupervised person re-identification aims to retrieve a given pedestrian image from unlabeled data. The method of clustering and assigning pseudo-labels has become mainstream, but there are still some problems that will reduce recognition accuracy. On the one hand, in the process of clustering, poor classification of hard samples between neighboring classes leads to inadequate clustering accuracy, which affects the quality of pseudo-labels. On the other hand, the representational capacity of features extracted by the backbone network is also crucial for the model’s performance. To this end, this paper proposes an unsupervised person re-identification method based on nearest neighbor sample constraint and ordinary differential equation guided feature reconstruction (NNSC-FR) to improve the clustering accuracy and pseudo-label quality while enhancing the representation of features. Specifically, we present a novel nearest neighbor sample constraint (NNSC) after neighbor sample mining for each instance sample to recognize the hard samples’ fine classification between classes. To further improve clustering accuracy, an inter-class balance loss (CB loss) is introduced to better identify the hard samples between the nearest neighbor classes. In addition, guided by the third-order adam solution of the Ordinary Differential Equation, we design a Feature Reconstruction (ODE-FR) module with residual structure to improve the model representation ability. Extensive experimental results on Market-1501, DukeMTMC-reID, and MSMT17 demonstrate that our proposed method is superior to the state-of-the-art methods.

Event camera-based person re-identification (Re-ID) effectively addresses the challenges faced by traditional Re-ID systems, such as privacy leakage, low-light imaging degradation, and motion blur. However, traditional Convolutional Neural Networks (CNNs) struggle to model long-range spatio-temporal dependencies, while the Transformer architecture encounters fundamental conflicts with second-order computational complexity and the high temporal resolution of event streams. Additionally, sparse data leads to wasted computational resources and diluted effective data. In contrast, the Mamba architecture, with its long-term modeling capability and linear complexity, is better suited for event stream data. Therefore, we innovatively explore the potential of VMamba in event camera-based person Re-ID; however, directly using VMamba does not fully leverage the temporal asynchronicity and spatial sparsity inherent in event data. To address this, we design a novel Sparse VMamba framework to construct a more robust spatio-temporal information extraction mechanism. First, we develop a Spatio-Temporal Information Modeling (STIM) module that simultaneously employs CNNs and Gated Recurrent Units (GRUs) for modeling spatial and temporal information. Then, we enhance the robustness of sparse data feature extraction using two strategies: on one hand, we utilize Anti-Noise Contour Enhancement (ANCE) module to improve motion contour features and mitigate sensor pulse noise; on the other hand, we implement Direction-Aware Sparse Perception (DASP) module to encourage the model to extract robust person descriptors. Results on the Event-ReID-v1 and Event-ReID-v2 datasets validate the effectiveness of our approach.

Multi-modal object Re-ID aims to leverage the complementary information provided by multiple modalities to overcome challenging conditions and achieve high-quality object matching. However, existing multi-modal methods typically rely on various modality interaction modules for information fusion, which can reduce the efficiency of real-time monitoring systems. Additionally, practical challenges such as low-quality multi-modal data or missing modalities further complicate the application of object Re-ID. To address these issues, we propose the Complementary Data Enhancement and Modal-Aware Soft Alignment Network (DESANet), which is designed to be independent of interactive networks and adaptable to scenarios with missing modalities. This approach ensures a simple-yet-effective, and efficient multi-modal object Re-ID. DESANet consists of three key components: Firstly, the Dual-Color Space Data Enhancement (DCDE) module, which enhances multi-modal data by performing patch rotation in the RGB space and improving image quality in the HSV space. Secondly, the Salient Feature ReConstruction (SFRC) module, which addresses the issue of missing modalities by reconstructing features from one modality using the other two. Thirdly, the Modal-Aware Soft Alignment (MASA) module, which integrates multi-source data to avoid the blind fusion of features and prevents the propagation of noise from reconstructed modalities. Our approach achieves state-of-the-art performances on both person and vehicle datasets. Source code is available at https://github.com/DWJ11/DESANet.

Video-based visible-infrared person re-identification (VVI-ReID) aims to match target pedestrians between visible and infrared videos, which is significantly applied in 24-hour surveillance systems. The key of VVI-ReID is to learn modality invariant and spatio-temporal invariant sequence-level representation to solve the challenges such as modality differences, spatio-temporal misalignment, and domain shift noise. However, existing methods predominantly emphasize on reducing modality discrepancy while relatively neglect temporal misalignment and domain shift noise reduction. To this end, this paper proposes a VVI-ReID framework called Feature Alignment Network (FA-Net) from the perspective of feature alignment, aiming to mitigate temporal misalignment. FA-Net comprises two main alignment modules: Spatial-Temporal Alignment Module (STAM) and Modality Distribution Constraint (MDC). STAM integrates global and local features to ensure individuals' spatial representation alignment. Additionally, STAM also establishes temporal relationships by exploring inter-frame features to address cross-frame person feature matching. Furthermore, we introduce the Modality Distribution Constraint (MDC), which utilizes a symmetric distribution loss to align the distributions of features from different modalities. Besides, the SAM Guidance Augmentation (SAM-GA) strategy is designed to transform the image space of RGB and IR frames to provide more informative and less noisy frame information. Extensive experimental results demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art methods. Our code will be available at: https://github.com/code/FANet.

Unsupervised person re-identification aims to retrieve a given pedestrian image from unlabeled data. For training on the unlabeled data, the method of clustering and assigning pseudo-labels has become mainstream, but the pseudo-labels themselves are noisy and will reduce the accuracy. To overcome this problem, several pseudo-label improvement methods have been proposed. But on the one hand, they only use target domain data for fine-tuning and do not make sufficient use of high-quality labeled data in the source domain. On the other hand, they ignore the critical fine-grained features of pedestrians and overfitting problems in the later training period. In this paper, we propose a novel unsupervised cross-domain person re-identification network (IDENet) based on an inter-domain equilibrium structure to improve the quality of pseudo-labels. Specifically, we make full use of both source domain and target domain information and construct a small learning network to equalize label allocation between the two domains. Based on it, we also develop a dynamic neural network with adaptive convolution kernels to generate adaptive residuals for adapting domain-agnostic deep fine-grained features. In addition, we design the network structure based on ordinary differential equations and embed modules to solve the problem of network overfitting. Extensive cross-domain experimental results on Market1501, PersonX, and MSMT17 prove that our proposed method outperforms the state-of-the-art methods.

Multimodal person reidentification (ReID), which aims to learn modality-complementary information by utilizing multimodal images simultaneously for person retrieval, is crucial for achieving all-time and all-weather monitoring. Existing methods try to address this issue through modality fusion to absorb complementary information. However, most of these methods are limited to the spatial domain only and usually overlook the intra-/intermodal interactions during feature fusion, resulting in insufficient learning of modality-specific and complementary information. To address these issues, we propose a tri-interaction enhancement network (TIENet), which contains three modules: spatial-frequency interaction (SFI), intermodal mask interaction (IMMI), and intramodal feature fusion (IMFF). Specifically, the SFI boosts the modality-specific representation by integrating the amplitude-guided attention mechanism into the phase space, combined with spatial-domain convolution to achieve fine-grained information learning. Meanwhile, the IMMI enhances the richness of the feature descriptors by embedding the intermodal relationships to preserve complementary information. Finally, the IMFF module considers the structure of the human body and integrates intramodal contextual information. Extensive experimental results demonstrate the effectiveness of our method, achieving superior performances on RGBNT201 and MARKET1501_RGBNT datasets.

Visible-infrared person re-identification (VI-ReID) is a challenging task because the different imaging principles of visible and infrared images bring about huge modality discrepancy. Existing methods primarily address this issue by generating intermediate images to align modality features and establish connections between the visible and infrared modalities. However, the quality of these generated images is often unstable, limiting the effectiveness of such approaches. To overcome this limitation, we propose a novel method called modality shared-specific features cooperative separation. It consists of two key modules: the saliency response module and the cooperative separation module, aimed at alleviating the modality gap. The saliency response module incorporates a location attention mechanism and local features to construct contextual connections and extract local saliency information. Then, the cooperative separation module employs a more concise dual-MLPs as generator to effectively separate shared-specific features. Additionally, we introduce a shared feature refinement mechanism in both the generator and discriminator. By coordinating the shared-specific features, our method achieves secondary separation and extracts purer modality-shared features without specific information. Extensive experiments conducted on the SYSU-MM01 and RegDB public datasets demonstrate that our proposed method performs excellently in VI-ReID.

Recently, with the exponentially increased amount of multi-view data in various fields such as Multimedia and bioinformatics, multi-view unsupervised feature selection has attracted much attention due to its necessity in dealing with high-dimensional features. Although previous approaches have achieved great success, they generally ignore the consistent information and the high-order connections among views. In this paper, we present a general multi-view unsupervised feature selection model which integrates the common graph learning and feature selection into a unified framework. Specifically, our approach first learns a pseudo label matrix for each view by preserving the local data structure, and then stack them into a third-order tensor with low-rank constraint to explore the high-order connections among the views. In order to exploit the consistent information among different views, we seek a consensus graph matrix with optimal cluster structure by taking advantage of the view-specific pseudo label matrices and the rank constraint. Meanwhile, we adopt the sparse regression model to select discriminative features under the guidance of the final pseudo labels obtained from the learned consensus graph. We introduce an alternate optimization algorithm ground on the alternating direction method of multipliers (ADMM) to optimize the presented method. Extensive experiments on both machine learning and single-cell multi-omics datasets prove the effectiveness of our method. Moreover, the case study carried out on an ovarian cancer dataset further confirms the applicability of our method in identifying non-redundant and representative features.