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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis
Po-Hsuan Huang, Jeng-Lin Li, Chin-Po Chen +2
Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural language. However, a major challe…
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen
Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, V…
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu +2
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on…
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang +1
Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal cha…
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation
Shang-Jui Kuo, Po-Han Huang, Chia-Ching Lin +2
Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is s…