AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding

2025-02-03

AI Summary

Current vision-language models suffer from semantic inconsistency in image-text alignment due to nonlinear mappings—e.g., MLPs—from visual features to the text embedding space, which induce distribution shifts and noise sensitivity. To address this, we propose a novel cross-modal alignment paradigm: dynamically projecting visual features into the large language model’s (LLM) text embedding space as a weighted average of LLM token embeddings, explicitly leveraging the LLM’s inherent linguistic priors to enhance semantic consistency—without updating the LLM’s parameters. This is the first work to adopt the LLM’s text embedding weighted average as the explicit visual projection target, achieved via a learnable weighting mechanism that ensures both robustness and interpretability. Our method achieves state-of-the-art performance on multi-document understanding benchmarks, significantly improving alignment quality while demonstrating superior robustness to image noise and layout variations.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• 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
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.

Problem

• Visual-Semantic Alignment
• Transformer Performance
• Image-Text Mismatch

Innovation

• AlignVLM
• Visual-Semantic Alignment
• Robustness