Chain-of-Focus: Adaptive Visual Search and Zooming for Multimodal Reasoning via RL

Vision language models (VLMs) have achieved impressive performance across a variety of computer vision tasks. However, the multimodal reasoning capability has not been fully explored in existing models. In this paper, we propose a Chain-of-Focus (CoF) method that allows VLMs to perform adaptive focusing and zooming in on key image regions based on obtained visual cues and the given questions, achieving efficient multimodal reasoning. To enable this CoF capability, we present a two-stage training pipeline, including supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage, we construct the MM-CoF dataset, comprising 3K samples derived from a visual agent designed to adaptively identify key regions to solve visual tasks with different image resolutions and questions. We use MM-CoF to fine-tune the Qwen2.5-VL model for cold start. In the RL stage, we leverage the outcome accuracies and formats as rewards to update the Qwen2.5-VL model, enabling further refining the search and reasoning strategy of models without human priors. Our model achieves significant improvements on multiple benchmarks. On the V* benchmark that requires strong visual reasoning capability, our model outperforms existing VLMs by 5% among 8 image resolutions ranging from 224 to 4K, demonstrating the effectiveness of the proposed CoF method and facilitating the more efficient deployment of VLMs in practical applications.

On Domain-Specific Post-Training for Multimodal Large Language Models

Recent years have witnessed the rapid development of general multimodal large language models (MLLMs). However, adapting general MLLMs to specific domains, such as scientific fields and industrial applications, remains less explored. This paper systematically investigates domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. (1) Data Synthesis: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs. (2) Training Pipeline: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. (3) Task Evaluation: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks. To support further research in MLLM domain adaptation, we will open-source our implementations.

CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update

Leveraging large language models (LLMs) to integrate off-the-shelf tools (e.g., visual models and image processing functions) is a promising research direction to build powerful visual assistants for solving diverse visual tasks. However, the learning capability is rarely explored in existing methods, as they freeze the used tools after deployment, thereby limiting the generalization to new environments requiring specific knowledge. In this paper, we propose CLOVA, a Closed-LOop Visual Assistant to address this limitation, which encompasses inference, reflection, and learning phases in a closed-loop framework. During inference, LLMs generate programs and execute corresponding tools to accomplish given tasks. The reflection phase introduces a multimodal global-local reflection scheme to analyze whether and which tool needs to be updated based on environmental feedback. Lastly, the learning phase uses three flexible manners to collect training data in real-time and introduces a novel prompt tuning scheme to update the tools, enabling CLOVA to efficiently learn specific knowledge for new environments without human involvement. Experiments show that CLOVA outperforms tool-usage methods by 5% in visual question answering and multiple-image reasoning tasks, by 10% in knowledge tagging tasks, and by 20% in image editing tasks, highlighting the significance of the learning capability for general visual assistants.

Enhance Reasoning Ability of Visual-Language Models via Large Language Models

Pre-trained visual language models (VLM) have shown excellent performance in image caption tasks. However, it sometimes shows insufficient reasoning ability. In contrast, large language models (LLMs) emerge with powerful reasoning capabilities. Therefore, we propose a method called TReE, which transfers the reasoning ability of a large language model to a visual language model in zero-shot scenarios. TReE contains three stages: observation, thinking, and re-thinking. Observation stage indicates that VLM obtains the overall information of the relative image. Thinking stage combines the image information and task description as the prompt of the LLM, inference with the rationals. Re-Thinking stage learns from rationale and then inference the final result through VLM.