Token Merging: Your ViT But Faster
Keywords: token merging, token pruning, inference speed, training speed, throughput, off-the-shelf, fine tuning
TL;DR: We merge tokens in a ViT at runtime using a fast custom matching algorithm. Our method, ToMe, can increase training and inference speed, lower training memory, and can be applied with and without training.
Abstract: 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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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/token-merging-your-vit-but-faster/code)
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