1 code implementation • 29 Apr 2024 • Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, Mike Zheng Shou
By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
no code implementations • 27 Apr 2024 • Qingyang Zhang, Yake Wei, Zongbo Han, Huazhu Fu, Xi Peng, Cheng Deng, QinGhua Hu, Cai Xu, Jie Wen, Di Hu, Changqing Zhang
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis.
no code implementations • 13 Feb 2024 • Zongbo Han, Yifeng Yang, Changqing Zhang, Linjun Zhang, Joey Tianyi Zhou, QinGhua Hu, Huaxiu Yao
The objective can be understood as seeking a model that fits the ground-truth labels by increasing the confidence while also maximizing the entropy of predicted probabilities by decreasing the confidence.
2 code implementations • 2 Feb 2024 • Zongbo Han, Zechen Bai, Haiyang Mei, Qianli Xu, Changqing Zhang, Mike Zheng Shou
Recent advancements in large vision-language models (LVLMs) have demonstrated impressive capability in visual information understanding with human language.
1 code implementation • 26 Nov 2023 • Yichen Bai, Zongbo Han, Changqing Zhang, Bing Cao, Xiaoheng Jiang, QinGhua Hu
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection.
no code implementations • 12 Aug 2023 • Zongbo Han, Tianchi Xie, Bingzhe Wu, QinGhua Hu, Changqing Zhang
Then a generic mixup regularization at the representation level is proposed, which can further regularize the model with the semantic information in mixed samples.
1 code implementation • CVPR 2023 • Mengyao Xie, Zongbo Han, Changqing Zhang, Yichen Bai, QinGhua Hu
Second, the quality of the imputed data itself is of high uncertainty.
no code implementations • 9 Apr 2023 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, QinGhua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.
no code implementations • 16 Nov 2022 • Mingcai Chen, Yu Zhao, Bing He, Zongbo Han, Bingzhe Wu, Jianhua Yao
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
1 code implementation • 19 Sep 2022 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
2 code implementations • 25 Apr 2022 • Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou
With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
no code implementations • 15 Jan 2022 • Yu Geng, Zongbo Han, Changqing Zhang, QinGhua Hu
Under the help of uncertainty, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated.
1 code implementation • CVPR 2022 • Zongbo Han, Fan Yang, Junzhou Huang, Changqing Zhang, Jianhua Yao
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
2 code implementations • CVPR 2022 • Bolian Li, Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang
To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework.
Ranked #19 on Long-tail Learning on CIFAR-10-LT (ρ=100)
1 code implementation • NeurIPS 2021 • Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications.
5 code implementations • ICLR 2021 • Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou
To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
no code implementations • 1 Jan 2021 • Zongbo Han, Changqing Zhang, Huazhu Fu, QinGhua Hu, Joey Tianyi Zhou
Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition.
no code implementations • 12 Nov 2020 • Changqing Zhang, Yajie Cui, Zongbo Han, Joey Tianyi Zhou, Huazhu Fu, QinGhua Hu
Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.
1 code implementation • NeurIPS 2019 • Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.