Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantly employ whole temporal sequences as their input, to the setting of online inference entails considerable computational redundancy. In this paper, we tackle this issue by reformulating the Spatio-Temporal Graph Convolutional Neural Network as a Continual Inference Network, which can perform step-by-step predictions in time without repeat frame processing. To evaluate our method, we create a continual version of ST-GCN, CoST-GCN, alongside two derived methods with different self-attention mechanisms, CoAGCN and CoS-TR. We investigate weight transfer strategies and architectural modifications for inference acceleration, and perform experiments on the NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets. Retaining similar predictive accuracy, we observe up to 109× reduction in time complexity, on-hardware accelerations of 26×, and reductions in maximum allocated memory of 52% during online inference.
Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatiotemporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high number of floating point operations (ranging from 16G to 100G FLOPs) to process a single sample, making their adoption in restricted computation application scenarios infeasible. In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network. We incorporate the TAM in a lightweight GCN topology to further reduce the overall number of computations. Experimental results on two benchmark datasets show that the proposed method outperforms with a large margin the baseline GCN-based method while having ×2.9 less number of computations. Moreover, it performs on par with the state-ofthe-art with up to ×9.6 less number of computations.
Graph convolutional networks have been very successful in skeletonbased human action recognition where the sequence of skeletons is modeled as a graph. However, most of the graph convolutional network-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods while it has much lower computational complexity.
Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN). In addition, context-dependent adaptive topology as a neighborhood vertex information and attention mechanism leverages a model to better represent actions. In this paper, we propose self-attention GCN hybrid model, Multi-Scale Spatial-Temporal self-attention (MSST)-GCN to effectively improve modeling ability to achieve state-of-the-art results on several datasets. We utilize spatial self-attention module with adaptive topology to understand intra-frame interactions within a frame among different body parts, and temporal self-attention module to examine correlations between frames of a node. These two are followed by multi-scale convolution network with dilations, which not only captures the long-range temporal dependencies of joints but also the long-range spatial dependencies (i.e., long-distance dependencies) of node temporal behaviors. They are combined into high-level spatial-temporal representations and output the predicted action with the softmax classifier.
The adsorption of H 2 Se molecule on AlN-NCS and AlP-NCS surfaces were investigated by using of DFT calculations. The potentials of Cl-functionalized AlN-NCS and AlP-NCS for H 2 Se adsorption were examined. All processes of H 2 Se-adsorption on considered nanocone sheets were exothermic reactions. The calculated |E ad | amount of complex H 2 Se with AlP-NCS was higher than AlN-NCS. The functionalization of considered nanocone sheets with Cl atom increase |E ad | amount of H 2 Se. Results reveal that, obtained E ad amounts of considered nanocone sheets have linear relationships with corresponding orbital energy amounts. Finally, the novel nanocone sheets with higher efficiency to adsorption of H 2 Se can be proposed.
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