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We propose a novel deep supervised neural network for the task of actionrecognition in videos, which implicitly takes advantage of visual tracking andshares the robustness of both deep Convolutional Neural Network (CNN) andRecurrent Neural Network (RNN). In our method, a multi-branch model is proposedto suppress noise from background jitters. Specifically, we firstly extractmulti-level deep features from deep CNNs and feed them into 3d-convolutionalnetwork. After that we feed those feature cubes into our novel joint LSTMmodule to predict labels and to generate attention regularization. We evaluateour model on two challenging datasets: UCF101 and HMDB51. The results show thatour model achieves the state-of-art by only using convolutional features.

Action Recognition with Joint Attention on Multi-Level Deep Features