Robust perception under low-visibility conditions requires fused imagery that jointly preserves infrared thermal saliency and polarization-derived structural details. However, existing infrared-polarization image fusion (IPIF) methods often overemphasize dominant infrared responses, causing weak yet informative polarization textures in dark regions to be suppressed. To address this issue, we propose IRPol-Fuse, an energy-structure coordinated IPIF framework for challenging low-visibility scenarios. The proposed framework contains three key modules: Polarization Attention Fusion for adaptive infrared-polarization allocation, Infrared Highlight Injector for highlight-guided infrared preservation, and Polarization Texture Injector for polarization texture restoration and fine-detail recovery. We further construct LI-PI, a dedicated infrared-polarization evaluation dataset for low-visibility and visually concealed scenes. Experiments on LI-PI and the public LDDRS dataset demonstrate that IRPol-Fuse achieves favorable performance in thermal target preservation, structural detail recovery, and visual naturalness. Region-aware evaluation and downstream object detection further verify that the proposed energy-structure coordination strategy effectively preserves both infrared target saliency and polarization-derived structural information. Code is available at https://github.com/1hzf/IRPolar-Fuse .
Zhuangfan Huang, Chusheng Fang, Xiaosong Li et al.· Infrared Physics & Techn...· 0 citations
Generalizable vehicle re-identification (ReID) seeks to develop models capable of adapting to previously unseen domains without additional fine-tuning or retraining. Most existing approaches attempt to learn domain-invariant representations by aligning data distributions across source domains. However, they often neglect the inherent domain-related redundancy within source images, which suppresses the learning of complementary features characterized by lower occurrence probabilities and weaker activations. To overcome this limitation, we introduce Unity in Diversity (UID), a framework of multi-expert knowledge adversarial learning and collaboration. UID incorporates a training-free mechanism to filter out domain-related redundancy in source images, thereby promoting the learning of complementary feature representations. Specifically, we design a Spectrum-based Transformation for Redundancy Elimination and Augmentation Module (STREAM), which generates two distinct types of image inputs for a two-stage complementary feature learning process. In the multi-expert knowledge adversarial learning phase, STREAM enables the model to acquire a diversified identity-oriented prompt set that captures subtle but discriminative visual cues critical for distinguishing highly similar vehicles. This multi-expert prompt set is progressively integrated into complementary feature representations through the proposed knowledge confrontation and collaboration mechanism, which substantially enhances the model's ability to extract fine-grained and complementary information. Extensive experiments conducted on multiple benchmarks demonstrate that UID achieves state-of-the-art performance, validating its effectiveness and generalizability. Our code is available at https://github.com/KZYYYY/UID.
Zhenyu Kuang, Hongyang Zhang, Xiaosong Li et al.· IEEE Transactions on Image P...· 0 citations
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