Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines (2410.21220v1)

Abstract: Search engines enable the retrieval of unknown information with texts. However, traditional methods fall short when it comes to understanding unfamiliar visual content, such as identifying an object that the model has never seen before. This challenge is particularly pronounced for large vision-LLMs (VLMs): if the model has not been exposed to the object depicted in an image, it struggles to generate reliable answers to the user's question regarding that image. Moreover, as new objects and events continuously emerge, frequently updating VLMs is impractical due to heavy computational burdens. To address this limitation, we propose Vision Search Assistant, a novel framework that facilitates collaboration between VLMs and web agents. This approach leverages VLMs' visual understanding capabilities and web agents' real-time information access to perform open-world Retrieval-Augmented Generation via the web. By integrating visual and textual representations through this collaboration, the model can provide informed responses even when the image is novel to the system. Extensive experiments conducted on both open-set and closed-set QA benchmarks demonstrate that the Vision Search Assistant significantly outperforms the other models and can be widely applied to existing VLMs.

• The paper introduces a novel framework integrating vision-language models with real-time web search using open-world Retrieval-Augmented Generation.
• It employs the Chain of Search algorithm to iteratively refine sub-questions and accurately source information from the web.
• Empirical results show significant improvements in factuality, relevance, and reasoning on both open-set and closed-set benchmarks.

Overview of Vision Search Assistant: Enhancing Multimodal Search Capabilities

The paper "Vision Search Assistant: Empower Vision-LLMs as Multimodal Search Engines" introduces an innovative framework designed to address the contemporary limitations of vision-LLMs (VLMs) when tasked with interpreting unfamiliar visual content. Traditional search engines, which excel at retrieving textual information, encounter significant hurdles when dealing with visual data, particularly images of objects they have never previously encountered. This issue is further compounded by the impracticality of continuously retraining VLMs due to the computationally intensive processes involved as novel objects and events continually emerge.

Core Contributions

The Vision Search Assistant (VSA) is proposed as a solution to these limitations, serving as a bridge between VLMs and web agents. This framework conducts an open-world Retrieval-Augmented Generation (RAG) via the web, thereby enhancing VLMs with real-time information access. The VSA framework allows VLMs to generate informed responses to user queries concerning images that are novel to the models. The paper elaborates on the collaborative mechanisms between visual content understanding by VLMs and real-time data retrieval by web agents, which together form the crux of this novel approach.

Significant contributions include:

• A novel framework that integrates vision-LLMs with web search capabilities through a web agent. This enables comprehensive multimodal knowledge retrieval and synthesis from the web.
• An innovative algorithm named Chain of Search is introduced. It facilitates a directed graph mechanism that iteratively gathers and refines sub-questions based on initial user queries, progressively sourcing knowledge from the web to generate satisfactory responses to visual queries.

Empirical Evidence

The efficacy of Vision Search Assistant is demonstrated through extensive experiments on both open-set and closed-set Question-Answering (QA) benchmarks. Notably, the VSA significantly outperformed existing models on various datasets. In open-set evaluations, the VSA showed superior performance across key objectives such as factuality, relevance, and supportiveness, as demonstrated by human expert evaluations. In closed-set evaluations on the LLaVA-W benchmark, the VSA showed notable improvements in conversation, detail, and reasoning tasks when evaluated against baseline models.

Key Insights and Implications

The development of the Vision Search Assistant brings forward significant implications for both theoretical and practical domains. Theoretical advancements include a deeper understanding of multimodal knowledge retrieval and synthesis, demonstrating how collaboration between visual and text-based models with real-time search capabilities can surmount the limitations of static pretrained models.

Practically, the VSA framework presents potential for diverse applications in real-world scenarios where rapid adaptation to novel visual inputs is needed, such as dynamic knowledge environments and real-time decision-making applications.

Future Directions

Future developments could potentially explore further optimization of the VSA framework to enhance inference speeds and retrieval efficiencies, addressing current limitations like inference speed and web condition dependencies. As the field progresses, expanding the VSA architecture to accommodate more complex multimodal interactions could enrich user experiences and automation capabilities of web agents.

In conclusion, this paper lays a foundational step towards transforming vision-LLMs into robust, multimodal search engines, with the capability to enhance real-world accessibility and functionality significantly. The Vision Search Assistant exemplifies a vital stride towards achieving greater interoperability between cutting-edge AI models and dynamic, real-time data environments.