Wuzhou Quan

Chenxing Meng, Wuzhou Quan, Yingjie Cai et al.

Cloud occlusion severely degrades the semantic integrity of optical remote sensing imagery. While incorporating Synthetic Aperture Radar (SAR) provides complementary observations, achieving efficient global modeling and reliable cross-modal fusion under cloud interference remains challenging. Existing methods rely on dense global attention to capture long-range dependencies, yet such aggregation indiscriminately propagates cloud-induced noise. Improving robustness typically entails enlarging model capacity, which further increases computational overhead. Given the large-scale and high-resolution nature of remote sensing applications, such computational demands hinder practical deployment, leading to an efficiency-reliability trade-off. To address this dilemma, we propose EDC, an efficiency-oriented and discrepancy-conditioned optical-SAR semantic segmentation framework. A tri-stream encoder with Carrier Tokens enables compact global context modeling with reduced complexity. To prevent noise contamination, we introduce a Discrepancy-Conditioned Hybrid Fusion (DCHF) mechanism that selectively suppresses unreliable regions during global aggregation. In addition, an auxiliary cloud removal branch with teacher-guided distillation enhances semantic consistency under occlusion. Extensive experiments demonstrate that EDC achieves superior accuracy and efficiency, improving mIoU by 0.56\% and 0.88\% on M3M-CR and WHU-OPT-SAR, respectively, while reducing the number of parameters by 46.7\% and accelerating inference by 1.98$\times$. Our implementation is available at https://github.com/mengcx0209/EDC.

Wuzhou Quan, Wei Zhao, Weiming Wang et al.

Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images. We propose HintU, a novel network to recover the local features lost by various UNet-based methods for effective infrared small target detection. HintU has two key contributions. First, it introduces the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge of target locations to highlight critical local features. Second, it improves the mainstream UNet-based architecture to preserve target pixels even after downsampling. HintU can shift the focus of various networks (e.g., vanilla UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels to a more restricted area from the beginning. Experimental results on three datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the performance of existing methods with only an additional 1.88 ms cost (on RTX Titan). Additionally, the explicit constraints of HintU enhance the generalization ability of UNet-based methods. Code is available at https://github.com/Wuzhou-Quan/HintU.

Muzhou Yang, Wuzhou Quan, Mingqiang Wei

Confidence alone is often misleading in hyperspectral image classification, as models tend to mistake high predictive scores for correctness while lacking awareness of uncertainty. This leads to confirmation bias, especially under sparse annotations or class imbalance, where models overfit confident errors and fail to generalize. We propose CABIN (Cognitive-Aware Behavior-Informed learNing), a semi-supervised framework that addresses this limitation through a closed-loop learning process of perception, action, and correction. CABIN first develops perceptual awareness by estimating epistemic uncertainty, identifying ambiguous regions where errors are likely to occur. It then acts by adopting an Uncertainty-Guided Dual Sampling Strategy, selecting uncertain samples for exploration while anchoring confident ones as stable pseudo-labels to reduce bias. To correct noisy supervision, CABIN introduces a Fine-Grained Dynamic Assignment Strategy that categorizes pseudo-labeled data into reliable, ambiguous, and noisy subsets, applying tailored losses to enhance generalization. Experimental results show that a wide range of state-of-the-art methods benefit from the integration of CABIN, with improved labeling efficiency and performance.

Haote Zhang, Lipeng Gu, Wuzhou Quan et al.

Visible-infrared object detection aims to enhance the detection robustness by exploiting the complementary information of visible and infrared image pairs. However, its performance is often limited by frequent misalignments caused by resolution disparities, spatial displacements, and modality inconsistencies. To address this issue, we propose the Wavelet-guided Misalignment-aware Network (WMNet), a unified framework designed to adaptively address different cross-modal misalignment patterns. WMNet incorporates wavelet-based multi-frequency analysis and modality-aware fusion mechanisms to improve the alignment and integration of cross-modal features. By jointly exploiting low and high-frequency information and introducing adaptive guidance across modalities, WMNet alleviates the adverse effects of noise, illumination variation, and spatial misalignment. Furthermore, it enhances the representation of salient target features while suppressing spurious or misleading information, thereby promoting more accurate and robust detection. Extensive evaluations on the DVTOD, DroneVehicle, and M3FD datasets demonstrate that WMNet achieves state-of-the-art performance on misaligned cross-modal object detection tasks, confirming its effectiveness and practical applicability.