  High-resolution images enable neural networks to learn richer visual
representations. However, this improved performance comes at the cost of
growing computational complexity, hindering their usage in latency-sensitive
applications. As not all pixels are equal, skipping computations for
less-important regions offers a simple and effective measure to reduce the
computation. This, however, is hard to be translated into actual speedup for
CNNs since it breaks the regularity of the dense convolution workload. In this
paper, we introduce SparseViT that revisits activation sparsity for recent
window-based vision transformers (ViTs). As window attentions are naturally
batched over blocks, actual speedup with window activation pruning becomes
possible: i.e., ~50% latency reduction with 60% sparsity. Different layers
should be assigned with different pruning ratios due to their diverse
sensitivities and computational costs. We introduce sparsity-aware adaptation
and apply the evolutionary search to efficiently find the optimal layerwise
sparsity configuration within the vast search space. SparseViT achieves
speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular
3D object detection, 2D instance segmentation, and 2D semantic segmentation,
respectively, with negligible to no loss of accuracy.
