Paper: https://arxiv.org/pdf/2411.10440)
Github: https://github.com/PKU-YuanGroup/LLaV...)
The paper introduces LLaVA-o1, a vision-language model designed for improved multi-stage reasoning. Unlike previous models, LLaVA-o1 independently performs summarization, visual interpretation, logical reasoning, and conclusion generation. This structured approach, facilitated by a new dataset and a stage-level beam search method, significantly enhances performance on various multimodal reasoning benchmarks, surpassing even larger, closed-source models. The authors demonstrate the effectiveness of their method through extensive experiments and ablation studies, highlighting the importance of structured reasoning and inference-time scaling for advanced VLM capabilities.
ai , computer vision , cv , peking university , artificial intelligence , arxiv , research , paper , publication , lvm , large visual models
