CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
arXiv:2605.23655v1 Announce Type: cross Abstract: High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods struggle with the trade-off between coverage and efficiency. Visual expert-assisted search is efficient but prone to blind spots when proposals fail, whereas scan-based search guarantees coverage at the cost of computational redundancy and semantic fragmentation. To address this dilemma, we introduce CVSearch, a training-free adaptive framework that dynamically schedules
The rapid advancement of MLLMs and their increasing application spaces are exposing the critical limitation of high-resolution image perception, necessitating immediate solutions.
Improving MLLM's ability to process high-resolution images unlocks new capabilities in complex visual understanding, crucial for deploying advanced AI in diverse sectors.
MLLMs will gain a significant boost in effectively interpreting detailed visual information, reducing the trade-off previously experienced between coverage and computational efficiency.
- · AI developers
- · Computer Vision sector
- · Robotics
- · Healthcare diagnostics
- · Legacy image processing techniques
- · Companies reliant on low-resolution visual inputs
More accurate and capable MLLMs become feasible for real-world applications requiring detailed visual analysis.
New AI products and services emerge that leverage the enhanced visual perception of MLLMs, impacting various industries from manufacturing to surveillance.
The development of highly performant MLLMs accelerates, potentially leading to more sophisticated autonomous systems and agentic AI architectures.
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Read at arXiv cs.LG