Search

Video prediction is a challenging but worth exploring task in computer vision. Different from image analysis, the challenge of video analysis derives from more complicated dependencies in time as well as in space. In this paper, we propose a Temporal-Spatial Attention Mechanism (TSAM) to capture not only spatial appearance dependencies but also temporal dynamic dependencies in video sequence. The TSAM is transplantable for existing networks and allows the long-range dependency modeling for various video analysis tasks (we take video prediction for specific experiments in this paper). Besides, we propose an additional Deep Perceptual Similarity Branch (DPSB) to encourage a better approximation to the ground-truth in high-level feature space, laying the foundation for frame generation. Extensive experiments on KTH, Penn Action and UCF-101 datasets demonstrate that our model performs quite competitively across diverse natural visual scenes, even in long-term video prediction.

In this paper, we introduce a joint model that learns to directly localize the temporal bounds of actions in untrimmed videos as well as precisely classify what actions occur. Most existing approaches tend to scan the whole video to generate action instances, which are really inefficient. Instead, inspired by human perception, our model is formulated based on a recurrent neural network to observe different locations within a video over time. And, it is capable of producing temporal localizations by only observing a fixed number of fragments, and the amount of computation it performs is independent of input video size. The decision policy for determining where to look next is learned by REINFORCE which is powerful in non-differentiable settings. In addition, different from relevant ways, our model runs localization and classification serially, and possesses a strategy for extracting appropriate features to classify. We evaluate our model on ActivityNet dataset, and it greatly outperforms the baseline. Moreover, compared with a recent approach, we show that our serial design can bring about 9% increase in detection performance.