42 citations · 69 across the 13 of their papers we have counts for
8 papers · 1 filter
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…