ToSA: Token Selective Attention for Efficient Vision Transformers

Understand

In this paper, we propose a novel token selective attention approach, ToSA, which can identify tokens that need to be attended as well as those that can skip a transformer layer.

• More specifically, a token selector parses the current attention maps and predicts the attention maps for the next layer, which are then used to select the important tokens that should participate in the attention operation.
• The remaining tokens simply bypass the next layer and are concatenated with the attended ones to re-form a complete set of tokens.
• In this way, we reduce the quadratic computation and memory costs as fewer tokens participate in self-attention while maintaining the features for all the image patches throughout the network, which allows it to be used for dense prediction tasks.