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Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures. [ABSTRACT FROM AUTHOR]

Skeleton-based hand gesture recognition is an active research topic in computer graphics and computer vision and has a wide range of applications in VR/AR and robotics. Although the spatial–temporal graph convolutional network has been successfully used in skeleton-based hand gesture recognition, these works often use a fixed spatial graph according to the hand skeleton tree or use a fixed graph on the temporal dimension, which may not be optimal for hand gesture recognition. In this paper, we propose a two-stream graph attention convolutional network with spatial–temporal attention for hand gesture recognition. We adopt pose stream and motion stream as the two input streams for our network. In pose stream, we use the joint in each frame as the input; In motion stream, we use the joint offsets between neighboring frames as the input. We propose a new temporal graph attention module to model the temporal dependency and also use a spatial graph attention module to construct dynamic skeleton graph. For each stream, we adopt graph convolutional network with spatial–temporal attention to extract the features. Then, we concatenate the feature of the pose stream and motion stream for gesture recognition. We achieve the competitive performance on the main hand gesture recognition benchmark datasets, which demonstrates the effectiveness of our method. [ABSTRACT FROM AUTHOR]