CoRe-UIE: Rethinking Coexisting and Region-wise Degradation for Underwater Image Enhancement

Underwater images often suffer from diverse and coexisting degradations, including color distortion, scattering haze, texture attenuation, and uneven illumination. These degradations vary across regions and may coexist locally, making conventional uniform restoration difficult to adapt to different degradation patterns. To address this problem, we propose Coexisting and Region-wise Degradation for Underwater Image Enhancement (\textbf{CoRe-UIE}), a degradation-oriented expert collaboration framework. CoRe-UIE combines a content-preserving shared expert with four shared-backbone routed experts for color correction, scattering suppression, texture recovery, and illumination protection. The routed experts share the same architecture but have independent parameters, and are assigned to different regions through input-derived degradation cues and region-adaptive Top-\(k\) routing. We further introduce a Hilbert--Schmidt Independence Criterion (HSIC)-based representation constraint to reduce statistical dependence among expert features and alleviate redundant expert responses. Experiments on UIEB, LSUI, and U45 demonstrate that CoRe-UIE achieves competitive quantitative performance and visually balanced enhancement under diverse underwater degradation conditions.

Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models

Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evaluations. Existing aggregation-based DP-ZO methods reconstruct model updates at a fixed scale, ignoring that the strength of useful signals varies throughout training. Consequently, noise-dominated updates may receive excessive weight and degrade model utility. To address this issue, we propose SAGE, a noise-aware shrinkage method that adaptively attenuates privatized estimates according to their estimated signal quality. SAGE subtracts the known Gaussian noise variance from the observed second moment to estimate the underlying signal energy, stabilizes this estimate through temporal tracking, and compares its current signal-to-noise level with a warm-up reference to derive a bounded shrinkage factor. As pure post-processing, SAGE requires neither additional privacy budget nor model queries and introduces only constant additional state. Our theoretical analysis shows that shrinkage reduces the quadratic update-risk term faster than the linear descent term, preserving useful descent while limiting the influence of noise-dominated updates. Experiments on RoBERTa-large, OPT-1.3B, and OPT-6.7B demonstrate that SAGE outperforms existing baselines in most settings under the same privacy budgets while preserving the forward-only memory efficiency of DP-ZO.

Staircase Recognition and Location Based on Polarization Vision

Staircase is one of the most common structures in artificial scenes. However, it is difficult for humanoid robots and people with lower limb disabilities or visual impairment to cross the scene without the help of sensors and intelligent algorithms. Staircase scene perception technology is a prerequisite for recognition and localization. This technology is of great significance for the mode switching of the robot and the calculation of the footprint position to adapt to the discontinuous terrain. However, there are still many problems that constrain the application of this technology, such as low recognition accuracy, high initial noise from sensors, unstable output signals and high computational requirements. In terms of scene reconstruction, the binocular and time of flight (TOF) reconstruction of the scene can be easily affected by environmental light and the surface material of the target object. In contrast, due to the special structure of the polarizer, the polarization can selectively transmit polarized light in a specific direction and this reconstruction method relies on the polarization information of the object surface. So the advantages of polarization reconstruction are reflected, which are less affected by environmental light and not dependent on the texture information of the object surface. In this paper, in order to achieve the detection of staircase, this paper proposes a contrast enhancement algorithm that integrates polarization and light intensity information, and integrates point cloud segmentation based on YOLOv11. To realize the high-quality reconstruction, we proposed a method of fusing polarized binocular and TOF depth information to realize the three-dimensional (3D) reconstruction of the staircase. Besides, it also proposes a joint calibration algorithm of monocular camera and TOF camera based on ICP registration and improved gray wolf optimization algorithm.