42 citations · 69 across the 13 of their papers we have counts for
13 papers
MedUAG: Unified Understanding and Generation for Medical Multimodal Models
Zijie Meng, Yuncheng Zhang, Hualiang Wang +8
Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the m…
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…
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
Zikai Xiao, Zihan Chen, Songshang Liu +7
Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task i…
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…
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Jianhong Bai, Zuozhu Liu, Hualiang Wang +4
Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the ma…
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its…
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision t…
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning
Hualiang Wang, Siming Fu, Xiaoxuan He +3
Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little at…
Towards Federated Long-Tailed Learning
Zihan Chen, Songshang Liu, Hualiang Wang +3
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of l…