3 papers
cs.CV2025
Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa
Large vision-language models (LVLMs) have demonstrated remarkable capabilities by integrating pre-trained vision encoders with large language models (LLMs). Similar to single-modal…
cs.LG2025
Zero-shot Concept Bottleneck Models
Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa +1
Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models, which explain their final label prediction by the intermediate prediction of h…
cs.AI2024
Explanation Bottleneck Models
Shin'ya Yamaguchi, Kosuke Nishida
Recent concept-based interpretable models have succeeded in providing meaningful explanations by pre-defined concept sets. However, the dependency on the pre-defined concepts restr…