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CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
[article]
2016
arXiv
pre-print
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,
arXiv:1608.00797v1
fatcat:ffawius2hfcs3dwklvwwxrhpue