Name
Abstract | ||
|---|---|---|
•We design a novel video saliency detection model by design the new 3-D ConvNet and B-ConvLSTM to extract short-term and long-term spatiotemporal cues, respectively. Through combining short-term and long-term spatiotemporal features, the proposed model can obtain promising performance for video saliency prediction.•We design a new two-layer B-ConvLSTM structure for long-term spatiotemporal feature extraction for video saliency detection. The proposed B-ConvLSTM can extract the temporal information not just from the previous video frames but also from the next frames, which demonstrates that the proposed network takes both the forward and backward temporal features into account. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2020.107294 | Pattern Recognition |
Keywords | DocType | Volume |
Video saliency detection,Spatiotemporal saliency,3D convolution network (3D-ConvNet),Bidirectional convolutional long-short term memory network (B-ConvLSTM) | Journal | 103 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.43 | 47 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuming Fang | 1 | 1247 | 75.50 |
| Chi Zhang | 2 | 145 | 44.61 |
| Xiongkuo Min | 3 | 337 | 40.88 |
| Hanqin Huang | 4 | 1 | 0.43 |
| Yugen Yi | 5 | 92 | 15.25 |
| Guangtao Zhai | 6 | 1707 | 145.33 |
| Chia-Wen Lin | 7 | 1639 | 120.23 |