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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…