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Author  |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro | ||||
| Title | Residual Stacked RNNs for Action Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 9th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
| Volume | Issue | Pages | 534-548 | ||
| Keywords | Action recognition; Deep residual learning; Two-stream RNN | ||||
| Abstract | Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. | ||||
| Address | Munich; September 2018 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LCE2018b | Serial | 3206 | ||
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