[2603.21232] QMoP: Query Guided Mixture-of-Projector for Efficient Visual Token Compression

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.21232 (cs) [Submitted on 22 Mar 2026] Title:QMoP: Query Guided Mixture-of-Projector for Efficient Visual Token Compression Authors:Zhongyang Li, Yaqian Li, Faming Fang, Rinyoichi Takezoe, Zi-Hao Bo, Cheng Qian, Mo Guang, Guixu Zhang, Kaiwen Long View a PDF of the paper titled QMoP: Query Guided Mixture-of-Projector for Efficient Visual Token Compression, by Zhongyang Li and 8 other authors View PDF HTML (experimental) Abstract:Multimodal large language models suffer from severe computational and memory bottlenecks, as the number of visual tokens far exceeds that of textual tokens. While recent methods employ projector modules to align and compress visual tokens into text-aligned features, they typically depend on fixed heuristics that limit adaptability across diverse scenarios. In this paper, we first propose Query Guided Mixture-of-Projector (QMoP), a novel and flexible framework that adaptively compresses visual tokens via three collaborative branches: (1) a pooling-based branch for coarse-grained global semantics, (2) a resampler branch for extracting high-level semantic representations, and (3) a pruning-based branch for fine-grained token selection to preserve critical visual detail. To adaptively coordinate these branches, we introduce the Query Guided Router (QGR), which dynamically selects and weights the outputs from different branches based on both visual input and textual queries. A Mixture-o...