Predicting Token Impact Towards Efficient Vision Transformer
Hong Wang Su Yang Xiaoke Huang Weishan Zhang
Token filtering to reduce irrelevant tokens prior to self-attention is a
straightforward way to enable efficient vision Transformer. This is the first
work to view token filtering from a feature selection perspective, where we
weigh the importance of a token according to how much it can change the loss
once masked. If the loss changes greatly after masking a token of interest, it
means that such a token has a significant impact on the final decision and is
thus relevant. Otherwise, the token is less important for the final decision,
so it can be filtered out. After applying the token filtering module
generalized from the whole training data, the token number fed to the
self-attention module can be obviously reduced in the inference phase, leading
to much fewer computations in all the subsequent self-attention layers. The
token filter can be realized using a very simple network, where we utilize
multi-layer perceptron. Except for the uniqueness of performing token filtering
only once from the very beginning prior to self-attention, the other core
feature making our method different from the other token filters lies in the
predictability of token impact from a feature selection point of view. The
experiments show that the proposed method provides an efficient way to approach
a 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 on
training from scratch.