Video object detection is more challenging compared to image object detection. Previous works proved that applying object detector frame by frame is not only slow but also inaccurate. Visual clues get weakened by defocus and motion blur, causing failure on corresponding frames. Multi-frame feature fusion methods proved effective in improving the accuracy, but they dramatically sacrifice the speed. Feature propagation based methods proved effective in improving the speed, but they sacrifice the accuracy. So is it possible to improve speed and performance simultaneously? Inspired by how human utilize impression to recognize objects from blurry frames, we propose Impression Network that embodies a natural and efficient feature aggregation mechanism. In our framework, an impression feature is established by iteratively absorbing sparsely extracted frame features. The impression feature is propagated all the way down the video, helping enhance features of low-quality frames. This impression mechanism makes it possible to perform long-range multi-frame feature fusion among sparse keyframes with minimal overhead. It significantly improves per-frame detection baseline on ImageNet VID while being 3 times faster (20 fps). We hope Impression Network can provide a new perspective on video feature enhancement. Code will be made available.
As shown by earlier analysis @cite_18 @cite_14 @cite_9 @cite_30 @cite_26 , consecutive video frames are highly similar, as well as their high-level convolutional features. This suggests that video sequences feature an inherent redundancy, which can be exploited to reduce time cost. In single image detectors @cite_11 @cite_13 @cite_1 @cite_17 @cite_6 , the heavy feature network (encoder) is much more costly than the task sub-network (decoder). Hence, when transplanting image detectors to videos, speed can by greatly improved by reusing the deep features of frames. Clockwork Convnets @cite_5 exploit the different evolve speed of features at different levels. By updating low and high level convolutional features at different frequency, it partially avoids redundant feature computation. It makes the network @math times faster, while sacrifices accuracy by @math 74.0
@cite_30 @cite_18 @cite_14 @cite_26 @cite_9 @cite_1 @cite_17 @cite_6 @cite_5 @cite_13 @cite_11
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Current methods for video description are based on encoder-decoder sentence generation using recurrent neural networks (RNNs). Recent work has demonstrated the advantages of integrating temporal attention mechanisms into these models, in which the decoder network predicts each word in the description by selectively giving more weight to encoded features from specific time frames. Such methods typically use two different types of features: image features (from an object classification model), and motion features (from an action recognition model), combined by naive concatenation in the model input. Because different feature modalities may carry task-relevant information at different times, fusing them by naive concatenation may limit the model's ability to dynamically determine the relevance of each type of feature to different parts of the description. In this paper, we incorporate audio features in addition to the image and motion features. To fuse these three modalities, we introduce a multimodal attention model that can selectively utilize features from different modalities for each word in the output description. Combining our new multimodal attention model with standard temporal attention outperforms state-of-the-art methods on two standard datasets: YouTube2Text and MSR-VTT. 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. Real-world video sequences coded at low bit rates suffer from compression artifacts, which are visually disruptive and can cause problems to computer vision algorithms. Unlike the denoising problem where the high frequency components of the signal are present in the noisy observation, most high frequency details are lost during compression and artificial discontinuities arise across the coding block boundaries. In addition to sparse spatial priors that can reduce the blocking artifacts for a single frame, temporal information is needed to recover the lost spatial details. However, establishing accurate temporal correspondences from the compressed videos is challenging because of the loss of high frequency details and the increase of false blocking artifacts. In this paper, we propose a non-causal temporal prior model to reduce video compression artifacts by propagating information from adjacent frames and iterating between image reconstruction and motion estimation. Experimental results on real-world sequences demonstrate that the deblocked videos by the proposed system have marginal statistics of high frequency components closer to those of the original ones, and are better input for standard edge and corner detectors than the coded ones. 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.