49 citations · 135 across the 18 of their papers we have counts for
19 papers
MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains
Leyan Xue, Changqing Zhang, Kecheng Xue +3
Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically e…
Retrieval-Augmented Prompt for OOD Detection
Ruisong Han, Zongbo Han, Jiahao Zhang +2
Out-of-Distribution (OOD) detection is crucial for the reliable deployment of machine learning models in-the-wild, enabling accurate identification of test samples that differ from…
Out-Of-Distribution Detection with Diversification (Provably)
Haiyun Yao, Zongbo Han, Huazhu Fu +3
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outli…
Confidence-aware multi-modality learning for eye disease screening
Ke Zou, Tian Lin, Zongbo Han +6
Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performa…
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Qingyang Zhang, Yake Wei, Zongbo Han +8
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 s…
Hallucination of Multimodal Large Language Models: A Survey
Zechen Bai, Pichao Wang, Tianjun Xiao +4
This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), wh…
Selective Learning: Towards Robust Calibration with Dynamic Regularization
Zongbo Han, Yifeng Yang, Changqing Zhang +3
Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which…
Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
Zongbo Han, Zechen Bai, Haiyang Mei +3
Recent advancements in large vision-language models (LVLMs) have demonstrated impressive capability in visual information understanding with human language. Despite these advances,…
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Yichen Bai, Zongbo Han, Changqing Zhang +3
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outl…
Semantic Equivariant Mixup
Zongbo Han, Tianchi Xie, Bingzhe Wu +2
Mixup is a well-established data augmentation technique, which can extend the training distribution and regularize the neural networks by creating ''mixed'' samples based on the la…
Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification
Mengyao Xie, Zongbo Han, Changqing Zhang +2
Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing inco…
Reweighted Mixup for Subpopulation Shift
Zongbo Han, Zhipeng Liang, Fan Yang +8
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…
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Zongbo Han, Zhipeng Liang, Fan Yang +7
Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but va…
Trusted Multi-View Classification with Dynamic Evidential Fusion
Zongbo Han, Changqing Zhang, Huazhu Fu +1
Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks.…
Uncertainty-Aware Multi-View Representation Learning
Yu Geng, Zongbo Han, Changqing Zhang +1
Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dim…
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Huan Ma, Zongbo Han, Changqing Zhang +3
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods…
Trustworthy Long-Tailed Classification
Bolian Li, Zongbo Han, Haining Li +2
Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and accordingly unpromising performance especially on tail class…
Trusted Multi-View Classification
Zongbo Han, Changqing Zhang, Huazhu Fu +1
Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehe…
Deep Partial Multi-View Learning
Changqing Zhang, Yajie Cui, Zongbo Han +3
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 diffe…