VLAD3: Encoding Dynamics of Deep Features for Action Recognition
Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activities that share sub-actions, and when dealing with untrimmed videos. Here, we propose a representation, VLAD for Deep Dynamics (VLAD^3), that accounts for different levels of video dynamics. It captures short-term dynamics with deep convolutional neural network features, relying on linear dynamic systems (LDS) to model medium-range dynamics. To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD^3 representation. An extensive evaluation was performed on Olympic Sports, UCF101 and THUMOS15, where the use of the VLAD^3 representation leads to state-of- the-art results.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionTemporal Action LocalizationSimilar Papers 제목 키워드 기반
Towards Good Practices for Action Video Encoding
High dimensional representations such as VLAD or FV have shown excellent accuracy in action recognition. This paper shows that a proper encoding built upon VLAD can achieve further accuracy boost with only negligible com…
Action RecognitionTemporal Action LocalizationTop-down Attention Recurrent VLAD Encoding for Action Recognition in Videos
Most recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification. For this to be successful, the…
Action RecognitionAction Recognition In VideosGeneral Classificationimage-classification+2Traffic scene recognition based on deep cnn and vlad spatial pyramids
Traffic scene recognition is an important and challenging issue in Intelligent Transportation Systems (ITS). Recently, Convolutional Neural Network (CNN) models have achieved great success in many applications, including…
Region ProposalScene ClassificationScene RecognitionMultiple VLAD encoding of CNNs for image classification
Despite the effectiveness of convolutional neural networks (CNNs) especially in image classification tasks, the effect of convolution features on learned representations is still limited. It mostly focuses on the salient…
ClassificationGeneral Classificationimage-classificationImage ClassificationEncoding CNN Activations for Writer Recognition
The encoding of local features is an essential part for writer identification and writer retrieval. While CNN activations have already been used as local features in related works, the encoding of these features has attr…
RetrievalWriter Retrieval