Sequential Video VLAD: Training the Aggregation Locally and Temporally

• Published in: IEEE transactions on image processing 2018-10, Vol.27 (10), p.4933-4944
• Main Authors: Youjiang Xu, Yahong Han, Hong, Richang, Qi Tian
• Format: Article
• Language: English
• Subjects: action recognition; Aggregates; Convolution; deep learning; Feature extraction; Image coding; recurrent convolution networks; Recurrent neural networks; Task analysis; video captioning; Video representation; Visualization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2018.2846664

As characterizing videos simultaneously from spatial and temporal cues has been shown crucial for the video analysis, the combination of convolutional neural networks and recurrent neural networks, i.e., recurrent convolution networks (RCNs), should be a native framework for learning the spatio-temporal video features. In this paper, we develop a novel sequential vector of locally aggregated descriptor (VLAD) layer, named SeqVLAD, to combine a trainable VLAD encoding process and the RCNs architecture into a whole framework. In particular, sequential convolutional feature maps extracted from successive video frames are fed into the RCNs to learn soft spatio-temporal assignment parameters, so as to aggregate not only detailed spatial information in separate video frames but also fine motion information in successive video frames. Moreover, we improve the gated recurrent unit (GRU) of RCNs by sharing the input-to-hidden parameters and propose an improved GRU-RCN architecture named shared GRU-RCN (SGRU-RCN). Thus, our SGRU-RCN has a fewer parameters and a less possibility of overfitting. In experiments, we evaluate SeqVLAD with the tasks of video captioning and video action recognition. Experimental results on Microsoft Research Video Description Corpus, Montreal Video Annotation Dataset, UCF101, and HMDB51 demonstrate the effectiveness and good performance of our method.