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Token filtering to reduce irrelevant tokens prior to self-attention is astraightforward way to enable efficient vision Transformer. This is the firstwork to view token filtering from a feature selection perspective, where weweigh the importance of a token according to how much it can change the lossonce masked. If the loss changes greatly after masking a token of interest, itmeans that such a token has a significant impact on the final decision and isthus relevant. Otherwise, the token is less important for the final decision,so it can be filtered out. After applying the token filtering modulegeneralized from the whole training data, the token number fed to theself-attention module can be obviously reduced in the inference phase, leadingto much fewer computations in all the subsequent self-attention layers. Thetoken filter can be realized using a very simple network, where we utilizemulti-layer perceptron. Except for the uniqueness of performing token filteringonly once from the very beginning prior to self-attention, the other corefeature making our method different from the other token filters lies in thepredictability of token impact from a feature selection point of view. Theexperiments show that the proposed method provides an efficient way to approacha light weighted model after optimized with a backbone by means of fine tune,which is easy to be deployed in comparison with the existing methods based ontraining from scratch.

Predicting Token Impact Towards Efficient Vision Transformer