LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation

TL;DR

LeMeViT introduces learnable meta tokens and a dual cross-attention mechanism to improve efficiency and speed in remote sensing image interpretation using Vision Transformers, achieving a better balance between computational cost and accuracy.

Contribution

This paper proposes a novel hierarchical Vision Transformer architecture with learnable meta tokens and dual cross-attention, enhancing efficiency and inference speed in remote sensing tasks.

Findings

LeMeViT achieves a 1.7x speedup over baseline models.

Fewer parameters with competitive performance.

Effective trade-off between efficiency and accuracy.

Abstract

Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtain sparse tokens by hand-crafted or parallel-unfriendly designs, posing a challenge to reach a better balance between efficiency and performance. Different from them, this paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information meanwhile improving the inference speed. Technically, the meta tokens are first initialized from image tokens via cross-attention. Then, we propose Dual Cross-Attention (DCA) to promote information exchange between image tokens and meta tokens, where they serve as query and key (value) tokens alternatively in a dual-branch structure, significantly reducing the computational complexity compared to self-attention. By employing DCA in the early stages with dense visual tokens, we obtain the hierarchical architecture LeMeViT with various sizes. Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1.7 \times$ speedup, fewer parameters, and competitive performance compared to the baseline models, and achieves a better trade-off between efficiency and performance.