Vision Search Assistant: Empower Vision-Language Models as Multimodal
  Search Engines
Authors: name: Zhixin Zhang, name: Yiyuan Zhang, name: Xiaohan Ding, name: Xiangyu Yue

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-language models (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.