MouSi: Poly-Visual-Expert Vision-Language Models
(2401.17221)Abstract
Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issues can limit the model's effectiveness in accurately interpreting complex visual information and over-lengthy contextual information. Addressing these challenges is crucial for enhancing the performance and applicability of VLMs. This paper proposes the use of ensemble experts technique to synergizes the capabilities of individual visual encoders, including those skilled in image-text matching, OCR, image segmentation, etc. This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs. In addition, we explore different positional encoding schemes to alleviate the waste of positional encoding caused by lengthy image feature sequences, effectively addressing the issue of position overflow and length limitations. For instance, in our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1. Experimental results demonstrate that VLMs with multiple experts exhibit consistently superior performance over isolated visual encoders and mark a significant performance boost as more experts are integrated. We have open-sourced the training code used in this report. All of these resources can be found on our project website.
Overview
-
The paper introduces a new approach in Vision-Language Models (VLMs) using an ensemble of visual experts to improve visual understanding.
-
Six pre-trained visual experts with different strengths are evaluated, followed by the development of multi-expert fusion networks to integrate their abilities.
-
Innovative solutions are proposed to tackle the issue of large numbers of vision tokens, including a multi-patch-one-token projection and varied positional encoding schemes.
-
Empirical results demonstrate that VLMs with multiple visual experts outperform standard models with single experts in multimodal tasks.
-
The study’s contributions pave the way for more sophisticated VLMs and suggest that poly-visual-expert VLMs have potential for even further improvements.
Introduction
Vision-Language Models (VLMs) have made notable advances, enabling machines to process and interpret complex visual and textual data. However, these multimodal systems often face limitations, notably the suboptimal performance of their visual components and the challenge of handling lengthy visual tokens. In this context, a novel approach is proposed utilizing ensemble experts to create poly-visual-expert VLMs. This method takes advantage of the specialized skills of various visual encoders to enrich the VLMs' visual understanding.
Architecture and Methodology
The comprehensive study begins by evaluating six pre-trained visual experts—CLIP, DINOv2, LayoutLMv3, Convnext, SAM, and MAE—each with distinct capabilities ranging from image-text matching to object segmentation. Subsequently, an integration technique is devised, leveraging multi-expert fusion networks to merge the individual strengths of these encoders effectively. The researchers focus on two key fusion methods, MLP projection and Q-Former, investigating the potential benefits of each for multi-channel signal transmission.
To further refine model efficiency, the problem of excessive vision token generation is addressed with innovative strategies, such as the multi-patch-one-token projection that compresses visual information, and the exploration of varied positional encoding schemes that offer a significant reduction in the positional embeddings required for visual tokens—an important innovation given the inherent position limitations within VLMs.
Experimental Results
The empirical results underscore the effectiveness of the poly-visual-expert approach. As the number of integrated experts increases, the VLMs displayed improved multimodal capabilities across multiple benchmarks. The findings indicate that VLMs with multiple experts outperform those with isolated visual encoders and achieve a significant performance boost, verified through an extensive set of benchmarks.
Contributions and Conclusion
The study’s contributions include the novel integration of diverse visual encoders into a cohesive model that better handles multimodal tasks, the introduction of efficient methods for encoding visual information, and the empirical validation of the model's superiority compared to existing models with single visual coding channels.
The evolutionary design and merging strategies take inspiration from biological visual systems, thus bringing VLMs a step closer to the complex and nuanced human-like understanding of multimodal information. The researchers believe that the potential of poly-visual-expert VLMs remains untapped, and with further data enhancement, these models can exhibit even greater performance, thereby consolidating the poly-visual-expert design as a promising direction in the development of advanced VLMs.
Create an account to read this summary for free: