V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs

The article introduces V*, a novel visual search mechanism for multimodal Language-and-Vision Models (MLLMs). This mechanism, guided by the world knowledge in LLMs, enhances the ability of MLLMs to focus on important visual details in high-resolution and visually crowded images. It improves collaborative reasoning, contextual understanding, and precise targeting of specific visual elements. This integration results in a new MLLM meta-architecture, named Show, sEArch, and TelL (SEAL).

To evaluate the performance of MLLMs in processing high-resolution images and focusing on visual details, the authors also developed V*Bench, a benchmark. The study underscores the importance of incorporating visual search capabilities into multimodal systems. The code for the study is available online.

Key takeaways:

The article introduces V*, a new LLM-guided visual search mechanism designed to improve the processing of high-resolution and visually crowded images in multimodal LLMs.
V* utilizes the world knowledge in LLMs for efficient visual querying, enhancing collaborative reasoning, contextual understanding, and precise targeting of specific visual elements.
The integration of V* results in a new MLLM meta-architecture, named Show, sEArch, and TelL (SEAL).
The authors have also developed V*Bench, a benchmark specifically designed to evaluate MLLMs in their ability to process high-resolution images and focus on visual details.

V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs

Key takeaways:

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