OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding, wherein processing audio-video token sequences creates a significant computational bottleneck, however. Existing token compression methods have yet to accommodate this emerging need of jointly compressing multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive empirical results demonstrate the merits of OmniZip - it achieves 3.42X inference speedup and 1.4X memory reduction over other top-performing counterparts, while maintaining performance with no training.
691d5b270247f3d258ef3261
Keda Tao
691d5b270247f3d258ef3262
Kele Shao
691d5b270247f3d258ef3263
Bohan Yu
691d5b270247f3d258ef3264
Weiqiang Wang
691d5b270247f3d258ef3265
Jian liu
691d5b270247f3d258ef3266
Huan Wang
OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding, wherein processing audio-video token sequences creates a significant computational bottleneck, however. Existing token compression methods have yet to accommodate this emerging need of jointly compressing multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive empirical results demonstrate the merits of OmniZip - it achieves 3.42X inference speedup and 1.4X memory reduction over other top-performing counterparts, while maintaining performance with no training.
OmniZip is a training-free framework that compresses audio-visual tokens by dynamically pruning video tokens based on audio retention scores, achieving significant inference speedup and memory reduction without sacrificing performance.
omnillms audio-visual token-compression salient audio tokens audio retention score video token pruning cross-modal similarity interleaved spatio-temporal scheme