  Vision Transformers (ViTs) have shown impressive performance in computer
vision, but their high computational cost, quadratic in the number of tokens,
limits their adoption in computation-constrained applications. However, this
large number of tokens may not be necessary, as not all tokens are equally
important. In this paper, we investigate token pruning to accelerate inference
for object detection and instance segmentation, extending prior works from
image classification. Through extensive experiments, we offer four insights for
dense tasks: (i) tokens should not be completely pruned and discarded, but
rather preserved in the feature maps for later use. (ii) reactivating
previously pruned tokens can further enhance model performance. (iii) a dynamic
pruning rate based on images is better than a fixed pruning rate. (iv) a
lightweight, 2-layer MLP can effectively prune tokens, achieving accuracy
comparable with complex gating networks with a simpler design. We assess the
effects of these design decisions on the COCO dataset and introduce an approach
that incorporates these findings, showing a reduction in performance decline
from ~1.5 mAP to ~0.3 mAP in both boxes and masks, compared to existing token
pruning methods. In relation to the dense counterpart that utilizes all tokens,
our method realizes an increase in inference speed, achieving up to 34% faster
performance for the entire network and 46% for the backbone.
