Multi-Granular Spatio-Temporal Token Merging for Training-Free
  Acceleration of Video LLMs
Authors: name: Jeongseok Hyun, name: Sukjun Hwang, name: Su Ho Han, name: Taeoh Kim, name: Inwoong Lee, name: Dongyoon Wee, name: Joon-Young Lee, name: Seon Joo Kim, name: Minho Shim

Video large language models (LLMs) achieve strong video understanding by
leveraging a large number of spatio-temporal tokens, but suffer from quadratic
computational scaling with token count. To address this, we propose a
training-free spatio-temporal token merging method, named STTM. Our key insight
is to exploit local spatial and temporal redundancy in video data which has
been overlooked in prior work. STTM first transforms each frame into
multi-granular spatial tokens using a coarse-to-fine search over a quadtree
structure, then performs directed pairwise merging across the temporal
dimension. This decomposed merging approach outperforms existing token
reduction methods across six video QA benchmarks. Notably, STTM achieves a
2times speed-up with only a 0.5% accuracy drop under a 50% token budget, and
a 3times speed-up with just a 2% drop under a 30% budget. Moreover, STTM is
query-agnostic, allowing KV cache reuse across different questions for the same
video. The project page is available at https://www.jshyun.me/projects/sttm.