  Recently, the efficient deployment and acceleration of powerful vision
transformers (ViTs) on resource-limited edge devices for providing multimedia
services have become attractive tasks. Although early exiting is a feasible
solution for accelerating inference, most works focus on convolutional neural
networks (CNNs) and transformer models in natural language processing
(NLP).Moreover, the direct application of early exiting methods to ViTs may
result in substantial performance degradation. To tackle this challenge, we
systematically investigate the efficacy of early exiting in ViTs and point out
that the insufficient feature representations in shallow internal classifiers
and the limited ability to capture target semantic information in deep internal
classifiers restrict the performance of these methods. We then propose an early
exiting framework for general ViTs termed LGViT, which incorporates
heterogeneous exiting heads, namely, local perception head and global
aggregation head, to achieve an efficiency-accuracy trade-off. In particular,
we develop a novel two-stage training scheme, including end-to-end training and
self-distillation with the backbone frozen to generate early exiting ViTs,
which facilitates the fusion of global and local information extracted by the
two types of heads. We conduct extensive experiments using three popular ViT
backbones on three vision datasets. Results demonstrate that our LGViT can
achieve competitive performance with approximately 1.8 $\times$ speed-up.
