most citedImproving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training

3 citations · 3 across the 10 of their papers we have counts for

collaborators

16 papers

cs.CV2025

Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining

Zhaocheng Yu, Kui Jiang, Junjun Jiang +3

Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining method…

cs.CV2025

AHDMIL: Asymmetric Hierarchical Distillation Multi-Instance Learning for Fast and Accurate Whole-Slide Image Classification

Jiuyang Dong, Jiahan Li, Junjun Jiang +2

Although multi-instance learning (MIL) has succeeded in pathological image classification, it faces the challenge of high inference costs due to the need to process thousands of pa…

cs.CV2025

NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces

Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano +36

This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhanc…

cs.CV2025

Boosting All-in-One Image Restoration via Self-Improved Privilege Learning

Gang Wu, Junjun Jiang, Kui Jiang +1

Unified image restoration models for diverse and mixed degradations often suffer from unstable optimization dynamics and inter-task conflicts. This paper introduces Self-Improved P…

cs.CV2025

Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather

Kui Jiang, Jing Cao, Zhaocheng Yu +2

Monocular depth estimation is critical for applications such as autonomous driving and scene reconstruction. While existing methods perform well under normal scenarios, their perfo…

cs.CV2025

VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement

Kui Jiang, Yan Luo, Junjun Jiang +3

State Space Models (SSMs) have emerged as a promising backbone for vision tasks due to their linear complexity and global receptive field. However, in the context of Underwater Ima…