Vision analysis tasks often experience substantial performance drops when processing images captured in lowlight environments. Existing approaches introduce infrared images to provide complementary information, and fusion strategies are consequently developed to combine the advantages of visible light images and infrared images. While fusion methods are typically designed to enhance visual quality with the expectation of improving task performance, many existing approaches tend to overemphasize perceptual fidelity, which can inadvertently compromise task-specific feature. To address this issue, we propose a task-driven hierarchical fusion (TDHF) framework designed to retain task-relevant information throughout the fusion process. Specifically, TDHF adopts a multi-scale hierarchical architecture to capture rich feature representations and incorporates a multi-head attention mechanism to model cross-modal interactions. In addition, we introduce a single-step denoising generation module that guides the fusion of infrared edge features and texture details from low-light images progressively. This process ultimately reconstructs the task-critical Y channel in the YCrCb color space. Extensive experiments on object detection under low-light conditions across four benchmark datasets demonstrate that TDHF effectively enhances task performance, achieving up to 1.1% higher mAP on LLVIP and 1.3% higher mAP on FLIR compared with state-of-the-art methods, without relying on excessive optimization of image quality (PSNR).

Remote fever screening based on thermal infrared images can screen feverish faces in real time and play an important role in vital sign monitoring. Screening methods that rely on short-term crowd facial temperature differences can overcome environmental effects without the need for additional sensors, but effectively encoding these differences to highlight fever face remains a challenge. To this end, we develop a fever screening framework based on Mamba, which exploits the context capturing capability of Mamba to model the temperature differences of thermal infrared face images. Furthermore, considering that local contextual associations in the crowd will limit the construction of global differences, we propose a shuffle scanning method to break the local contextual associations and construct multiple shuffle scanning to achieve global difference representation. In addition, we design a temperature-aware self-supervised loss function to cope with the situation where data of fever faces is difficult to collect. Finally, we achieve state-of-the-art performance in both supervised and self-supervised cases on the thermal infrared face dataset.

With the advancement of vehicle-to-vehicle (V2V) ad hoc networks and wireless communication technologies, mobile edge caching has become a key enabler for enhancing network performance and user experience. However, traditional federated learning-based collaborative caching approaches in vehicular scenarios suffer from inadequate client selection mechanisms and limited prediction accuracy, which result in suboptimal cache hit ratios and increased content transmission latency. To address these challenges, we propose a Digital Twin-based Asynchronous Federated Learning-driven Predictive Edge Caching with Deep Reinforcement Learning (DAPR) framework. DAPR employs an intelligent client selection strategy based on asynchronous federated learning, which leverages mobility prediction and data quality assessment to avoid selecting highly mobile clients or clients with low-quality data, thereby significantly improving model convergence efficiency. In addition, we design a GRU-VAE prediction model that uses a Variational Autoencoder (VAE) to capture latent data distribution features and Gated Recurrent Units (GRUs) to model temporal dependencies, thereby substantially enhancing the accuracy of content request prediction. The predicted content popularities are then fed into a deep reinforcement learning-driven caching decision engine to dynamically optimize edge caching resource allocation. Extensive experiments demonstrate that DAPR achieves superior performance in terms of average reward, cache hit ratio, and transmission latency, thereby effectively improving the overall efficiency of vehicular edge caching systems.

Physiological activities can be manifested by the sensitive changes in facial imaging. While they are barely observable to our eyes, computer vision manners can, and the derived remote photoplethysmography (rPPG) has shown considerable promise. However, existing studies mainly rely on spatial skin recognition and temporal rhythmic interactions, so they focus on identifying explicit features under ideal light conditions, but perform poorly in-the-wild with intricate obstacles and extreme illumination exposure. In this paper, we propose an end-to-end video transformer model for rPPG. It strives to eliminate complex and unknown external time-varying interferences, whether they are sufficient to occupy subtle biosignal amplitudes or exist as periodic perturbations that hinder network training. In the specific implementation, we utilize global interference sharing, subject background reference, and self-supervised disentanglement to eliminate interference, and further guide learning based on spatiotemporal filtering, reconstruction guidance, and frequency domain and biological prior constraints to achieve effective rPPG. To the best of our knowledge, this is the first robust rPPG model for real outdoor scenarios based on natural face videos, and is lightweight to deploy. Extensive experiments show the competitiveness and performance of our model in rPPG prediction across datasets and scenes.

Utilizing thermal infrared facial imaging for fever screening in public spaces has become a common strategy to curb the spread of influenza viruses. However, it is difficult to capture larger number of faces with fever labels, which makes learning facial temperature representation extremely difficult. To overcome this limitation, we propose a self-supervised fever screening framework (SelfFS) to learn temperature representation from infrared face images. Specifically, SelfFS employs rate reduction theory to guide the network to focus on temperature features by expanding the coding rate of faces with different temperatures and compressing the coding rate of faces with the same temperature but different appearances. Furthermore, we impose sparsity constraints on the network parameters, which facilitates the extraction of simple temperature features with a limited number of neurons while filtering complex appearance features. Experiments demonstrate that our SelfFS framework outperforms existing fever screening techniques and achieves the comparable results with the supervised methods.

Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epidermal specular reflections induced by head movements and facial expressions. Furthermore, diffuse reflections of light in the skin-colored subcutaneous tissue caused by multiple time-varying physiological signals independent of breathing are entangled with the intention of the respiratory process, leading to confusion in current research. To address these issues, this article proposes a novel network for natural light video-based remote respiration estimation. Specifically, our model consists of a two-stage architecture that progressively implements vital measurements. The first stage adopts an encoder-decoder structure to recharacterize the facial motion frame differences of the input video based on the gradient binary state of the respiratory signal during inspiration and expiration. Then, the obtained generative mapping, which is disentangled from various time-varying interferences and is only linearly related to the respiratory state, is combined with the facial appearance in the second stage. To further improve the robustness of our algorithm, we design a targeted long-term temporal attention module and embed it between the two stages to enhance the network's ability to model the breathing cycle that occupies ultra many frames and to mine hidden timing change clues. We train and validate the proposed network on a series of publicly available respiration estimation datasets, and the experimental results demonstrate its competitiveness against the state-of-the-art breathing and physiological prediction frameworks.