Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models Zuyan Liu, Yuhao Dong, Yongming Rao, Jie Zhou, and Jiwen Lu introduce Chain-of-Spot (CoS), an interactive reasoning method to enhance the visual reasoning ability of Large Vision-Language Models (LVLMs). CoS focuses on key regions of interest (ROI) in images, corresponding to the posed questions or instructions, allowing LVLMs to access more detailed visual information without altering the original image resolution. By integrating CoS with instruct-following LLaVA1.5 models, the process of image reasoning consistently improves performance across a wide range of multimodal datasets and benchmarks, achieving new state-of-the-art results. Empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content, paving the way for more sophisticated visual instruction-following applications. The method involves an interactive question-answering framework that compels LVLMs to integrate global image and localized ROI features. The process begins by identifying the ROI relevant to the question, then using both the global image features and localized ROI features to deduce the final answer. This approach is validated through extensive experiments on various visual question answering and multimodal benchmarks, showing significant improvements in performance metrics. For example, on the VQAv2 dataset, accuracy increased from 80.0% to 81.8%, and on the GQA dataset, it increased from 63.3% to 64.8%. The method also shows improvements on other benchmarks such as VizWiz, SEEDBench, and MM-Vet. The Chain-of-Spot approach is implemented through a detailed pipeline that includes data annotation, fine-tuning, and inference procedures. The method leverages attention mechanisms to identify the ROI in images, constructing a relevance map to highlight the most relevant regions. The results demonstrate that the method significantly enhances the performance of LVLMs, particularly in complex visual recognition tasks. The approach is also effective in reducing computational costs while maintaining image resolution, making it a viable and efficient solution for enhancing detail without prohibitive computational costs. The method's effectiveness is further supported by qualitative comparisons and visualizations, showing the model's ability to selectively crop and spotlight the most salient region within an image that directly pertains to the answer.Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models Zuyan Liu, Yuhao Dong, Yongming Rao, Jie Zhou, and Jiwen Lu introduce Chain-of-Spot (CoS), an interactive reasoning method to enhance the visual reasoning ability of Large Vision-Language Models (LVLMs). CoS focuses on key regions of interest (ROI) in images, corresponding to the posed questions or instructions, allowing LVLMs to access more detailed visual information without altering the original image resolution. By integrating CoS with instruct-following LLaVA1.5 models, the process of image reasoning consistently improves performance across a wide range of multimodal datasets and benchmarks, achieving new state-of-the-art results. Empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content, paving the way for more sophisticated visual instruction-following applications. The method involves an interactive question-answering framework that compels LVLMs to integrate global image and localized ROI features. The process begins by identifying the ROI relevant to the question, then using both the global image features and localized ROI features to deduce the final answer. This approach is validated through extensive experiments on various visual question answering and multimodal benchmarks, showing significant improvements in performance metrics. For example, on the VQAv2 dataset, accuracy increased from 80.0% to 81.8%, and on the GQA dataset, it increased from 63.3% to 64.8%. The method also shows improvements on other benchmarks such as VizWiz, SEEDBench, and MM-Vet. The Chain-of-Spot approach is implemented through a detailed pipeline that includes data annotation, fine-tuning, and inference procedures. The method leverages attention mechanisms to identify the ROI in images, constructing a relevance map to highlight the most relevant regions. The results demonstrate that the method significantly enhances the performance of LVLMs, particularly in complex visual recognition tasks. The approach is also effective in reducing computational costs while maintaining image resolution, making it a viable and efficient solution for enhancing detail without prohibitive computational costs. The method's effectiveness is further supported by qualitative comparisons and visualizations, showing the model's ability to selectively crop and spotlight the most salient region within an image that directly pertains to the answer.