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Wenxue Li

7 accepted papers

2025

AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

AAAI 2025technical

Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limitations arise from two inherent challenges in real-world LIE: 1) the collection of distorted/clean image pairs is often i…

Cited by 7SourcePDFScholar
2025

Detect Any Mirrors: Boosting Learning Reliability on Large-Scale Unlabeled Data with an Iterative Data Engine

CVPR 2025poster

Mirror detection is a challenging task because a mirror's visual appearance varies depending on the reflected content. Due to limited annotated data, current methods failed to generalize well for detecting diverse mirror scenes. Semi-supervised learning with large-scale unlabeled data can improve ge…

2025

GlassWizard: Harvesting Diffusion Priors for Glass Surface Detection

ICCV 2025poster

Glass Surface Detection (GSD) is a critical task in computer vision, enabling precise interactions with transparent surfaces and enhancing both safety and object recognition accuracy. However, current research still faces challenges in both recognition performance and generalization capability. Than…

Cited by 0SourcePDFScholar
2025

Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation

AAAI 2025technical

In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing studies typically employ semi-supervised segmentation techniques using pseudo-labeling and consistency regularization.…

Cited by 2SourcePDFScholar
2025

PromptHaze: Prompting Real-world Dehazing via Depth Anything Model

AAAI 2025technical

Real-world image dehazing remains a challenging task due to the diverse nature of haze degradation and the lack of large-scale paired datasets. Existing methods based on hand-crafted priors or generative priors struggle to recover accurate backgrounds and fine details from dense haze regions. In thi…

Cited by 0SourcePDFScholar
2025

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding

CVPR 2025poster

Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations. In this work, hallucinations are categorized into two main types: initial hallucinations and snowball hallucinations.…

Cited by 0SourcePDFScholar
2025

Towards Realistic Semi-supervised Medical Image Classification

AAAI 2025technical

Existing semi-supervised learning (SSL) approaches follow the idealized closed-world assumption, neglecting the challenges present in realistic medical scenarios, such as open-set distribution and imbalanced class distribution. Although some methods in natural domains attempt to address the open-set…

Cited by 0SourcePDFScholar