In-Person Oral presentation / top 5% paper

Token Merging: Your ViT But Faster

Daniel Bolya · Cheng-Yang Fu · Xiaoliang Dai · Peizhao Zhang · Christoph Feichtenhofer · Judy Hoffman

[ Abstract ] [ Livestream: Visit Oral 1 Track 1: Deep Learning and representational learning I ]
Mon 1 May 1:10 a.m. — 1:20 a.m. PDT

We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518 models on images and 2.2x the throughput of ViT-L on video with only a 0.2-0.3% accuracy drop in each case. ToMe can also easily be applied during training, improving in practice training speed up to 2x for MAE fine-tuning on video. Training with ToMe further minimizes accuracy drop, leading to 2x the throughput of ViT-B on audio for only a 0.4% mAP drop. Qualitatively, we find that ToMe merges object parts into one token, even over multiple frames of video. Overall, ToMe’s accuracy and speed are competitive with state-of-the-art on images, video, and audio.

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