Name
Title | ||
|---|---|---|
Action Detection Based on 3D Convolution Neural Network with Channel Attention Mechanism |
Abstract | ||
|---|---|---|
In this paper, we propose an action detection model, a simple yet effective combination of channel attention mechanism with 3D convolution neural network, by which to enhance the performance of feature extraction in the video. Channel attention module uses the channel information in the feature extraction process of our 3D convolutional network to efficaciously pick out the features that are essential for the task and suppress useless ones. The proposed model can effectively promote the spatiotemporal feature representation power in the video. We embed our feature extraction model into the R-C3D framework to test the performance of our method by conducting comparative experiments on the THUMOS'14 dataset. Experimental results indicate that the proposed method can authentically enhance the accuracy of action detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSCI47803.2020.9308342 | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | DocType | ISBN |
action detection,channel attention,3D convolutional neural networks,R-C3D | Conference | 978-1-7281-2548-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Gao | 1 | 0 | 0.34 |
| Huilai Liang | 2 | 0 | 0.34 |
| Bao-Di Liu | 3 | 166 | 27.34 |
| Yanjiang Wang | 4 | 15 | 8.65 |