3 papers
cs.CV2026
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
cs.CV2025
Hierarchical Process Reward Models are Symbolic Vision Learners
Shan Zhang, Aotian Chen, Kai Zou +3
Symbolic computer vision represents diagrams through explicit logical rules and structured representations, enabling interpretable understanding in machine vision. This requires fu…
cs.CV2025
Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs
Shan Zhang, Aotian Chen, Yanpeng Sun +6
Current multimodal large language models (MLLMs) often underperform on mathematical problem-solving tasks that require fine-grained visual understanding. The limitation is largely…