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Convolutional Two-Stream Network Fusion for Video Action Recognition
TLDR
In this paper, a spatial and temporal network can be fused at the last convolution layer without loss of performance, but with a substantial saving in parameters, and furthermore, pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance.Abstract:
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters; (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy; finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results.read more
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Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
TL;DR: This study focuses on the motion and appearance information as two important orthogonal components of a video, and proposes Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN, which generates more plausible motion videos and achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works.
Journal ArticleDOI
Attention module-based spatial–temporal graph convolutional networks for skeleton-based action recognition
TL;DR: A model of dynamic skeletons called attention module-based-ST-GCN is proposed, which solves problems of skeleton-based action recognition by adding attention module, which brings stronger expressive power and generalization capability.
Journal ArticleDOI
Sequential fusion of facial appearance and dynamics for depression recognition
TL;DR: It is shown that a chained-fusion mechanism introduced to jointly learn facial appearance and dynamics in a unified framework can provide a probabilistic perspective of the model correlation and complementarity between two different data modalities for improved depression recognition.
Journal ArticleDOI
Discovering spatio-temporal action tubes
TL;DR: Wang et al. as mentioned in this paper proposed a tracking-by-point-matching algorithm to stitch the discrete action regions into a continuous spatio-temporal action tube and used R3DCNN to predict action categories and determine temporal boundaries of the generated tubes.
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Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition.
TL;DR: This paper proposes to develop compact RNN models using Hierarchical Tucker (HT) decomposition, and shows that the proposed HT-based LSTM (HT-LSTM), consistently achieves simultaneous and significant increases in both compression ratio and test accuracy on different datasets.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
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