CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
This addresses a bottleneck in MLLMs for researchers and practitioners by enhancing fine-grained image analysis, though it is incremental as it builds on existing MLLM frameworks.
The paper tackles the problem of fine-grained multi-modal understanding in Multi-modal Large Language Models (MLLMs) by proposing a Coarse to Fine (CoF) approach, which improves visual grounding and boosts baseline model performance with notable generalization.
The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely linked to the models' visual grounding capabilities. The restricted spatial awareness and perceptual acuity of visual encoders frequently lead to interference from irrelevant background information in images, causing the models to overlook subtle but crucial details. As a result, achieving fine-grained regional visual comprehension becomes difficult. In this paper, we break down multi-modal understanding into two stages, from Coarse to Fine (CoF). In the first stage, we prompt the MLLM to locate the approximate area of the answer. In the second stage, we further enhance the model's focus on relevant areas within the image through visual prompt engineering, adjusting attention weights of pertinent regions. This, in turn, improves both visual grounding and overall performance in downstream tasks. Our experiments show that this approach significantly boosts the performance of baseline models, demonstrating notable generalization and effectiveness. Our CoF approach is available online at https://github.com/Gavin001201/CoF.