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20242026
most citedDinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

3 citations · 3 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency

Jia Guo, Jiawei Du, Shengzhu Yang +21

Current retinal foundation models remain constrained by curated research datasets that lack authentic clinical context, and require extensive task-specific optimization for each ap…

cs.CV2025

Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection

Jia Guo, Shuai Lu, Lei Fan +9

Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the…

cs.CV2025

CLIPin: A Non-contrastive Plug-in to CLIP for Multimodal Semantic Alignment

Shengzhu Yang, Jiawei Du, Shuai Lu +3

Large-scale natural image-text datasets, especially those automatically collected from the web, often suffer from loose semantic alignment due to weak supervision, while medical da…

cs.CV2025

MDSAM:Memory-Driven Sparse Attention Matrix for LVLMs Hallucination Mitigation

Shuaiye Lu, Linjiang Zhou, Xiaochuan Shi

Hallucinations in large vision-language models (LVLMs) often stem from the model's sensitivity to image tokens during decoding, as evidenced by attention peaks observed when genera…

cs.CV2025

MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data

Yuqin Dai, Zhouheng Yao, Chunfeng Song +7

Brain decoding aims to reconstruct visual perception of human subject from fMRI signals, which is crucial for understanding brain's perception mechanisms. Existing methods are conf…