Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

2025-05-22

AI Summary

This study addresses the fine-grained alignment problem between visual embeddings and large language models (LLMs) in multimodal language models (MLLMs). Existing vision-language projectors suffer from coarse-grained semantic modeling of visual tokens and compression-induced distortion. To overcome these limitations, we propose the “multi-semantic alignment” hypothesis and design a lightweight patch-level alignment training paradigm that establishes more precise semantic mappings between visual patches and word embeddings. Our method leverages joint analysis of a pre-trained vision encoder and an LLM, incorporating patch-level supervised fine-tuning (SFT). Experiments demonstrate substantial improvements: +16% in referring expression localization, +4% in visual question answering, +3% on instruction-following benchmarks, and marked enhancement in image captioning quality. These results validate the effectiveness of fine-grained, patch-level alignment in bridging the semantic gap between vision and language modalities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent models that rely on powerful pretrained vision encoders and LLMs. A common approach to connect the pretrained vision encoder and LLM is through a projector applied after the vision encoder. However, the projector is often trained to enable the LLM to generate captions, and hence the mechanism by which LLMs understand each vision token remains unclear. In this work, we first investigate the role of the projector in compressing vision embeddings and aligning them with word embeddings. We show that the projector significantly compresses visual information, removing redundant details while preserving essential elements necessary for the LLM to understand visual content. We then examine patch-level alignment -- the alignment between each vision patch and its corresponding semantic words -- and propose a *multi-semantic alignment hypothesis*. Our analysis indicates that the projector trained by caption loss improves patch-level alignment but only to a limited extent, resulting in weak and coarse alignment. To address this issue, we propose *patch-aligned training* to efficiently enhance patch-level alignment. Our experiments show that patch-aligned training (1) achieves stronger compression capability and improved patch-level alignment, enabling the MLLM to generate higher-quality captions, (2) improves the MLLM's performance by 16% on referring expression grounding tasks, 4% on question-answering tasks, and 3% on modern instruction-following benchmarks when using the same supervised fine-tuning (SFT) setting. The proposed method can be easily extended to other multimodal models.

Problem

• Enhancing alignment between vision embeddings and LLMs for MLLMs
• Investigating projector's role in compressing and aligning vision embeddings
• Improving patch-level alignment via patch-aligned training for better MLLM performance

Innovation

• Investigates projector role in vision-LLM alignment
• Proposes multi-semantic alignment hypothesis for patches
• Introduces patch-aligned training for better compression