AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding
Ahmed Masry, Juan A. Rodriguez, Tianyu Zhang, Suyuchen Wang, Chao Wang, Aarash Feizi, Akshay Kalkunte Suresh, Abhay Puri, Xiangru Jian, Pierre-André Noël, Sathwik Tejaswi Madhusudhan, Marco Pedersoli, Bang Liu, Nicolas Chapados, Yoshua Bengio, Enamul Hoque, Christopher Pal, Issam H. Laradji, David Vazquez, Perouz Taslakian, Spandana Gella, Sai Rajeswar
2025-02-04
Summary
This paper talks about AlignVLM, a new method that helps AI systems better connect images with text. It focuses on improving how vision-language models (VLMs) align visual data with language, making them more accurate for tasks like understanding scanned documents.
What's the problem?
Vision-language models often struggle to link what they see in images with the words they know. Current methods, like using multilayer perceptrons (MLPs), can produce errors or mismatches between the visual and text information, making it hard for the AI to process tasks that require understanding both images and text together.
What's the solution?
The researchers developed AlignVLM, which maps visual features to a weighted average of text embeddings from large language models (LLMs). This approach uses the language knowledge already built into the LLM to guide how visual features are connected to text. AlignVLM ensures that the AI interprets images in a way that makes sense linguistically. It was tested on tasks like document understanding and showed better performance than previous methods, while also being more resistant to noise.
Why it matters?
This research is important because it improves how AI systems handle tasks that involve both images and text, which is essential for many real-world applications. By making these systems more accurate and reliable, AlignVLM could lead to better tools for document analysis, image captioning, and answering questions about visuals. This advancement could benefit fields like education, business, and accessibility.
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.