  Spatial and temporal features are two key and complementary information for
human action recognition. In order to make full use of the intra-frame spatial
characteristics and inter-frame temporal relationships, we propose the
Squeeze-and-Excitation Long-term Recurrent Convolutional Networks (SE-LRCN) for
human action recognition. The Squeeze and Excitation operations are used to
implement the feature recalibration. In SE-LRCN, Squeeze-and-Excitation
ResNet-34 (SE-ResNet-34) network is adopted to extract spatial features to
enhance the dependencies and importance of feature channels of pixel
granularity. We also propose the Squeeze-and-Excitation Long Short-Term Memory
(SE-LSTM) network to model the temporal relationship, and to enhance the
dependencies and importance of feature channels of frame granularity. We
evaluate the proposed model on two challenging benchmarks, HMDB51 and UCF101,
and the proposed SE-LRCN achieves the competitive results with the
state-of-the-art.
