RDI-Net: Relational Dynamic Inference Networks

Huanyu Wang, Songyuan Li, Shihao Su, Zequn Qin, Xi Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4621-4630

Dynamic inference networks, aimed at promoting computational efficiency, go along an adaptive executing path for a given sample. Prevalent methods typically assign a router for each convolutional block and sequentially make block-by-block executing decisions, without considering the relations during the dynamic inference. In this paper, we model the relations for dynamic inference from two aspects: the routers and the samples. We design a novel type of router called the relational router to model the relations among routers for a given sample. In principle, the current relational router aggregates the contextual features of preceding routers by graph convolution and propagates its router features to subsequent ones, making the executing decision for the current block in a long-range manner. Furthermore, we model the relation between samples by introducing a Sample Relation Module (SRM), encouraging correlated samples to go along correlated executing paths. As a whole, we call our method the Relational Dynamic Inference Network (RDI-Net). Extensive experiments on CIFAR-10/100 and ImageNet show that RDI-Net achieves state-of-the-art performance and computational cost reduction. Our code and models will be made publicly available.