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Action recognition greatly benefits motion understanding in video analysis.Recurrent networks such as long short-term memory (LSTM) networks are a popularchoice for motion-aware sequence learning tasks. Recently, a convolutionalextension of LSTM was proposed, in which input-to-hidden and hidden-to-hiddentransitions are modeled through convolution with a single kernel. This impliesan unavoidable trade-off between effectiveness and efficiency. Herein, wepropose a new enhancement to convolutional LSTM networks that supportsaccommodation of multiple convolutional kernels and layers. This resembles aNetwork-in-LSTM approach, which improves upon the aforementioned concern. Inaddition, we propose an attention-based mechanism that is specifically designedfor our multi-kernel extension. We evaluated our proposed extensions in asupervised classification setting on the UCF-101 and Sports-1M datasets, withthe findings showing that our enhancements improve accuracy. We also undertookqualitative analysis to reveal the characteristics of our system and theconvolutional LSTM baseline.

Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos