Multi-modal object Re-identification refers to the task of identifying the same object from different cameras according to multi-modal cues. The existing multi-modal object Re-ID methods have achieved remarkable progress in extracting discriminative global and local representations. However, confined to learning generic static features, these approaches lack the relational structures and deep semantic reasoning needed for complex alignment, rendering them susceptible to modality noise as they passively aggregate information without filtering interference. To solve these problems, an LSR2 framework is imposed to actively mine relational and reasoning patterns. Specifically, the Representation Learning with Frequency Mining (RFM) module purifies features via frequency-domain decoupling. The Relational Expert System with Selective Reasoning (RER) then leverages expert guidance to adaptively filter relational cues. Finally, a Structural Consistency Constraint (SCC) enforces topological alignment to reduce high-dimensional mapping ambiguity. Extensive experiments on three object re-identification benchmark sets verify that by successfully learning rich relational and reasoning representations, the proposed method overcomes traditional modality noise and achieves superior performance.

Generative content is increasingly applied in VR to provide immersive experiences, yet maintaining high generation quality remains challenging for audiovisual effects. Particularly in dynamic fluid phenomena, achieving realism and presence requires adherence to physical laws. To accomplish this objective, this work proposes an audiovisual synergy fluid animation generation framework, which enhances immersion by improving motion texture fidelity and audiovisual consistency. It comprises Detail-Enhanced Texture generator (DET) and Physics-Guided Audio generator (PGA). DET integrates Global-Local Physics guidance (GLP) and Temporal Texture Modeling (TTM) to produce video textures, explicitly optimizing dynamic details by leveraging local motion cues and assigned cumulative differences. PGA incorporates Visual Semantic Augmenter (VSA) and Rhythm Semantic Adapter (RSA) to synchronize audio by fusing static visual semantics with dynamic motion semantics to improve temporal coherence. By integrating DET and PGA, this framework strengthens audiovisual immersion in VR natural dynamic scenes from both visual and auditory perspectives. Quantitative and qualitative evaluations demonstrate that our approach surpasses most existing methods in terms of texture realism and audiovisual synchronization, offering new insights for advancing immersive experiences in dynamic VR phenomena.

Action recognition is a promising task of identifying human activities in videos or images. Human movement is often accompanied by occlusion and blurring, resulting in many approximate behaviors that cannot be correctly recognized. Most methods directly employ local or motion cues to improve global features. Due to insufficient exploration of 3-D depth information, similar actions from the 2-D perspective still cannot be particularity distinguished. To tackle this challenge, this article proposes a novel action recognition approach termed RPS-Net, integrating 3-D human body reconstruction and hybrid pose self-supervision (HPSS). The designed 3-D reconstruction network is named CFFormer, which leveraging a context-fusion structure to generate 3-D meshes as auxiliary input. Among them, the mesh rotated 90° (M9) is exploited to provide multiperspective associative information, which employing spatial residual learning to enhance extra 3-D cues under various interpose variations. Meanwhile, the original images and meshes from the same perspective will perform HPSS with temporal encoding. It is responsible for capturing subtle differences in key point positions and structures across multiple dimensions. Extensive experiments demonstrate that our proposed method significantly surpasses most existing algorithms. And the Top-1 recognition accuracy on SSV2, HMDB51, and Olympic Sports datasets can reach 73.4%, 87.7%, and 96.2%, respectively.

Visible-infrared person re-identification (VI-ReID) is a critical cross-modality fine-grained classification task with significant implications for public safety and security applications. Existing VI-ReID methods primarily focus on extracting modality-invariant features for person retrieval. However, due to the inherent lack of texture information in infrared images, these modality-invariant features tend to emphasize global contexts. Consequently, individuals with similar silhouettes are often misidentified, posing potential risks to security systems and forensic investigations. To address this problem, this paper innovatively introduces natural language descriptions to learn the global-local contexts for VI-ReID. Specifically, we design a framework that jointly optimizes visible-infrared alignment plus (VIAP) and visual-textual reasoning (VTR), and introduces local-global joint measure (LJM) to enhance the metric, while proposing a human-LLM collaborative approach to incorporate textual descriptions into existing cross-modal person re-identification datasets. VIAP achieves cross-modal alignment between RGB and IR. It can explicitly utilize designed frequency-aware modality alignment and relationship-reinforced fusion to explore the potential of local cues in global features and modality-invariant information. VTR proposes pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. LJM proposes introducing local feature distances into the measure stage metric to enhance the relevance of matching using fine-grained information. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches. The dataset is publicly available athttps://github.com/qyx596/vireid-caption.

Visible-infrared person re-identification (VI-ReID) is a cross-modality fine-grained classification task. Existing approaches for VI-ReID mainly explore modality-invariant features for person retrieval. However, modality-invariant features pay more attention to global contexts, due to the lack of texture information in infrared images. This leads to a person with similar silhouette often being misidentified. Targeting this problem, this paper innovatively introduces natural language specification to learn global-local contexts for VI-ReID. Specifically, our framework jointly optimizes visible-infrared alignment (VIA) and visual-textual reasoning (VTR). VIA achieves cross-modal between RGB and IR. It can explicitly utilize designed modality-guided alignment and relationship-reinforced fusion to explore the potential of local cues in global features. VTR proposes the pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches.