5 papers
Structure-Aware Prototype Guided Trusted Multi-View Classification
Haojian Huang, Jiahao Shi, Zhe Liu +4
Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inc…
Text-Visual Semantic Constrained AI-Generated Image Quality Assessment
Qiang Li, Qingsen Yan, Haojian Huang +3
With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prev…
Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification
Haojian Huang, Chuanyu Qin, Zhe Liu +6
Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process.…
Towards Robust Uncertainty-Aware Incomplete Multi-View Classification
Mulin Chen, Haojian Huang, Qiang Li
Handling incomplete data in multi-view classification is challenging, especially when traditional imputation methods introduce biases that compromise uncertainty estimation. Existi…
Evidential Deep Partial Multi-View Classification With Discount Fusion
Haojian Huang, Zhe Liu, Sukumar Letchmunan +3
Incomplete multi-view data classification poses significant challenges due to the common issue of missing views in real-world scenarios. Despite advancements, existing methods ofte…