Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for multirate videos, in which actions or subactions occur at different speeds. The frame sampling rate should vary in accordance with the different motion speeds. In this work, we propose a simple yet effective strategy, termed random temporal skipping, to address this situation. This strategy effectively handles multirate videos by randomizing the sampling rate during training. It is an exhaustive approach, which can potentially cover all motion speed variations. Furthermore, due to the large temporal skipping, our network can see video clips that originally cover over 100 frames. Such a time range is enough to analyze most actions events. We also introduce an occlusion-aware optical flow learning method that generates improved motion maps for human action recognition. Our framework is end-to-end trainable, runs in real-time, and achieves state-of-the-art performance on six widely adopted video benchmarks.
To handle multirate videos, there are two widely adopted approaches. One is to train multiple models, each of them corresponding to a different fixed frame-rate. This is similar to using image pyramids to handle the multi-scale problem in image analysis. The other is to generate sliding windows of different lengths for each video (a.k.a, temporal jittering), with the hope of capturing temporal invariance. However, neither of these approaches is exhaustive, and they are both computationally intensive. @cite_27 is the most similar work to ours since they deal with motion speed variance. However, our work differs in several aspects. First, we aim to explicitly learn the transitions between frames while @cite_27 uses past and future neighboring video clips as the temporal context, and reconstruct the two temporal transitions. Their objective is considerably harder to optimize, which may lead to sub-optimal solutions. Second, our random skipping strategy is easy to implement without computational overhead whereas the image reconstruction of @cite_27 will lead to significant computational burden. Third, their proposed multirate gated recurrent unit only works in RNNs, while our strategy is generally applicable.
@cite_27
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Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present-past transition and present-future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4 on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset. Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present @math past transition and present @math future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4 on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset. Video analysis is an important branch of computer vision due to its wide applications, ranging from video surveillance, video indexing, and retrieval to human computer interaction. All of the applications are based on a good video representation, which encodes video content into a feature vector with fixed length. Most existing methods treat video as a flat image sequence, but from our observations we argue that video is an information-intensive media with intrinsic hierarchical structure, which is largely ignored by previous approaches. Therefore, in this work, we represent the hierarchical structure of video with multiple granularities including, from short to long, single frame, consecutive frames (motion), short clip, and the entire video. Furthermore, we propose a novel deep learning framework to model each granularity individually. Specifically, we model the frame and motion granularities with 2D convolutional neural networks and model the clip and video granularities with 3D convolutional neural networks. Long Short-Term Memory networks are applied on the frame, motion, and clip to further exploit the long-term temporal clues. Consequently, the whole framework utilizes multi-stream CNNs to learn a hierarchical representation that captures spatial and temporal information of video. To validate its effectiveness in video analysis, we apply this video representation to action recognition task. We adopt a distribution-based fusion strategy to combine the decision scores from all the granularities, which are obtained by using a softmax layer on the top of each stream. We conduct extensive experiments on three action benchmarks (UCF101, HMDB51, and CCV) and achieve competitive performance against several state-of-the-art methods. Video super-resolution (SR) aims to generate a highresolution (HR) frame from multiple low-resolution (LR) frames in a local temporal window. The inter-frame temporal relation is as crucial as the intra-frame spatial relation for tackling this problem. However, how to utilize temporal information efficiently and effectively remains challenging since complex motion is difficult to model and can introduce adverse effects if not handled properly. We address this problem from two aspects. First, we propose a temporal adaptive neural network that can adaptively determine the optimal scale of temporal dependency. Filters on various temporal scales are applied to the input LR sequence before their responses are adaptively aggregated. Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network which is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner. Our proposed models with learned temporal dynamics are systematically evaluated on public video datasets and achieve state-of-the-art SR results compared with other recent video SR approaches. Both of the temporal adaptation and the spatial alignment modules are demonstrated to considerably improve SR quality over their plain counterparts.