Projects

Video Understanding

Video Action Recognition

 
  • S. Sun, Z. Kuang, L. Sheng, W. Ouyang and W. Zhang.
    Optical flow guided feature: A fast and robust motion representation for video action recognition.
    Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018. [paper] [code]

    We introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow, and could be embedded in any existing CNN based video action recognition framework with only a slight additional cost.

 
  • Z. Zhang, Z. Kuang, P. Luo, L. Feng and W. Zhang.
    Temporal sequence distillation: Towards few-frame action recognition in videos.
    Proc. ACM Multimedia (MM), 2018. [paper]

    Video Analytics Software as a Service (VA SaaS) has been rapidly growing in recent years. VA SaaS is typically accessed by users using a lightweight client. Because the transmission bandwidth between the client and cloud is usually limited and expensive, it brings great benefits to design cloud video analysis algorithms with a limited data transmission requirement. As the first attempt in this direction, this work introduces a problem of few-frame action recognition, which aims at maintaining high recognition accuracy, when accessing only a few frames during both training and test.

Video Summarization

 
  • L. Feng, Z. Li, Z. Kuang and W. Zhang.
    Extractive video summarizer with memory augmented neural networks.
    Proc. ACM Multimedia (MM), 2018.

    Humans usually create a summary after viewing and understanding the whole video, and the global attention mechanism capturing information from all video frames plays a key role in the summarization process. Motivated by this observation, we proposed a memory augmented extractive video summarizer, which utilizes an external memory to record visual information of the whole video with high capacity. With the external memory, the video summarizer simply predicts the importance score of a video shot based on the global understanding of the video frames.







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