6 citations · 11 across the 7 of their papers we have counts for
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MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models
Yuan Wang, Hualiang Wang, Yixin Chen +6
Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging. Existing approaches ei…
Robustness-Guided Image Synthesis for Data-Free Quantization
Jianhong Bai, Yuchen Yang, Huanpeng Chu +7
Quantization has emerged as a promising direction for model compression. Recently, data-free quantization has been widely studied as a promising method to avoid privacy concerns, w…
Towards Distribution-Agnostic Generalized Category Discovery
Jianhong Bai, Zuozhu Liu, Hualiang Wang +7
Data imbalance and open-ended distribution are two intrinsic characteristics of the real visual world. Though encouraging progress has been made in tackling each challenge separate…
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Hualiang Wang, Yi Li, Huifeng Yao +1
Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable eff…
Uniformly Distributed Category Prototype-Guided Vision-Language Framework for Long-Tail Recognition
Siming Fu, Xiaoxuan He, Xinpeng Ding +2
Recently, large-scale pre-trained vision-language models have presented benefits for alleviating class imbalance in long-tailed recognition. However, the long-tailed data distribut…
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Marawan Elbatel, Hualiang Wang, Robert Martí +2
In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly…