Related papers: Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration

Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration

URL: http://arxiv.org/abs/2411.17686v1
Date: Tue, 26 Nov 2024 18:53:51 GMT
Title: Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
Authors: Yuhang Han, Xuyang Liu, Pengxiang Ding, Donglin Wang, Honggang Chen, Qingsen Yan, Siteng Huang,
Abstract summary: This study rethinks the current landscape of training-free token reduction research. We propose a unified ''filter-correlate-compress'' paradigm that decomposes the token reduction into three distinct stages. Experimental results across 10 benchmarks indicate that our methods can achieve up to an 82.4% reduction in FLOPs.
Score: 31.50005609235654
License:
Abstract: To accelerate the inference of heavy Multimodal Large Language Models (MLLMs), this study rethinks the current landscape of training-free token reduction research. We regret to find that the critical components of existing methods are tightly intertwined, with their interconnections and effects remaining unclear for comparison, transfer, and expansion. Therefore, we propose a unified ''filter-correlate-compress'' paradigm that decomposes the token reduction into three distinct stages within a pipeline, maintaining consistent design objectives and elements while allowing for unique implementations. We additionally demystify the popular works and subsume them into our paradigm to showcase its universality. Finally, we offer a suite of methods grounded in the paradigm, striking a balance between speed and accuracy throughout different phases of the inference. Experimental results across 10 benchmarks indicate that our methods can achieve up to an 82.4% reduction in FLOPs with a minimal impact on performance, simultaneously surpassing state-of-the-art training-free methods. Our project page is at https://ficoco-accelerate.github.io/.