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• W2950687050 abstract "In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks with learnable spatio-temporal feature aggregation. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as out-performs other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks." @default.
• W2950687050 created "2019-06-27" @default.
• W2950687050 creator A5004353237 @default.
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• W2950687050 date "2017-04-10" @default.
• W2950687050 modified "2023-09-27" @default.
• W2950687050 title "ActionVLAD: Learning spatio-temporal aggregation for action classification" @default.
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• W2950687050 hasPublicationYear "2017" @default.
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