Automated detection of road damage (ADRD) is a challenging topic in road maintenance. It focuses on automatically detecting road damage and assessing severity by deep learning. Because of the sparse distribution of characteristic pixels, it is more challenging than object detection. Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). The dataset is collected by professional onboard cameras and is manually labeled in eight categories with three different degrees (mild, moderate and severe), which can effectively help promote research of automated detection of road damage. At the same time, a novel baseline with attention fusion and normalization is proposed to evaluate and analyze the published dataset. It explicitly leverages edge detection cues to guide attention for salient regions and suppresses the weights of non-salient features by attention normalization, which can alleviate the interference of sparse pixel distribution on damage detection. Experimental results demonstrate that the proposed baseline significantly outperforms most existing methods on the existing RDD2020 dataset and the newly released CNRDD dataset. Further, the CNRDD dataset is proved more robust, as its high damage density and professional classification are more conducive to promote the development of ADRD.

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 at https://github.com/qyx596/vireid-caption.

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.

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.