Name
Abstract | ||
|---|---|---|
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segment networks and further raise the performance via a number of other techniques. Specifically, we use the latest deep model architecture, e.g., ResNet and Inception V3, and introduce new aggregation schemes (top-k and attention-weighted pooling). Additionally, we incorporate the audio as a complementary channel, extracting relevant information via a CNN applied to the spectrograms. With these techniques, we derive an ensemble of deep models, which, together, attains a high classification accuracy (mAP $93.23%$) on the testing set and secured the first place in the challenge. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Data mining,Spectrogram,Model architecture,Computer science,Pooling,Communication channel,Artificial intelligence,Residual neural network,Machine learning |
DocType | Volume | Citations |
Journal | abs/1608.00797 | 4 |
PageRank | References | Authors |
0.52 | 10 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanjun Xiong | 1 | 331 | 18.71 |
| LiMin Wang | 2 | 816 | 48.41 |
| Zhe Wang | 3 | 199 | 19.26 |
| Bowen Zhang | 4 | 80 | 4.49 |
| Hang Song | 5 | 16 | 2.28 |
| Wei Li | 6 | 74 | 5.27 |
| Dahua Lin | 7 | 1117 | 72.62 |
| Yu Qiao | 8 | 2267 | 152.01 |
| Luc Van Gool | 9 | 27566 | 1819.51 |
| Xiaoou Tang | 10 | 15728 | 670.19 |