  Most existing Convolutional Neural Networks(CNNs) used for action recognition
are either difficult to optimize or underuse crucial temporal information.
Inspired by the fact that the recurrent model consistently makes breakthroughs
in the task related to sequence, we propose a novel Multi-Level Recurrent
Residual Networks(MRRN) which incorporates three recognition streams. Each
stream consists of a Residual Networks(ResNets) and a recurrent model. The
proposed model captures spatiotemporal information by employing both
alternative ResNets to learn spatial representations from static frames and
stacked Simple Recurrent Units(SRUs) to model temporal dynamics. Three
distinct-level streams learned low-, mid-, high-level representations
independently are fused by computing a weighted average of their softmax scores
to obtain the complementary representations of the video. Unlike previous
models which boost performance at the cost of time complexity and space
complexity, our models have a lower complexity by employing shortcut connection
and are trained end-to-end with greater efficiency. MRRN displays significant
performance improvements compared to CNN-RNN framework baselines and obtains
comparable performance with the state-of-the-art, achieving 51.3% on HMDB-51
dataset and 81.9% on UCF-101 dataset although no additional data.
