Two-stream convolutional neural networks show great promise for action recognition tasks. However, most two-stream based approaches train the appearance and motion subnetworks independently, which may lead to the decline in performance due to the lack of interactions among two streams. To overcome this limitation, we propose a Spatiotemporal Distilled Dense-Connectivity Network (STDDCN) for video action recognition. This network implements both knowledge distillation and dense-connectivity (adapted from DenseNet). Using this STDDCN architecture, we aim to explore interaction strategies between appearance and motion streams along different hierarchies. Specifically, block-level dense connections between appearance and motion pathways enable spatiotemporal interaction at the feature representation
layers. Moreover, knowledge distillation among two streams (each treated as a student) and their last fusion (treated as teacher) allows both streams to interact at the high level layers. The special architecture of STDDCN allows it to gradually obtain effective hierarchical spatiotemporal features. Moreover, it can be trained end-to-end. Finally, numerous ablation studies validate the effectiveness and generalization of our model on two benchmark datasets, including UCF101 and HMDB51. Simultaneously, our model achieves promising performances.