In this work we present a new efficient approach to Human Action Recognition called Video Transformer Network (VTN). It leverages the latest advances in Computer Vision and Natural Language Processing and applies them to video understanding. The proposed method allows us to create lightweight CNN models that achieve high accuracy and real-time speed using just an RGB mono camera and general purpose CPU. Furthermore, we explain how to improve accuracy by distilling from multiple models with different modalities into a single model. We conduct a comparison with state-of-the-art methods and show that our approach performs on par with most of them on famous Action Recognition datasets. We benchmark the inference time of the models using the modern inference framework and argue that our approach compares favorably with other methods in terms of speed accuracy trade-off, running at 56 FPS on CPU. The models and the training code are available.
Another popular group of methods is related to the use of 3D primitives like 3D Convolution, 3D Batch Normalization, 3D Pooling, and others. They generalize original operations introducing an additional dimension @math , which indicates the sequence of frames. One of the first architectures that leveraged these primitives for the application to AR, is C3D @cite_29 . Another famous 3D CNN, which saturated UCF-101 benchmark @cite_45 , is I3D @cite_52 . It benefits from pre-training on a large-scale ImageNet @cite_35 dataset by inflating trained 2D filters into 3D. Although methods based on 3D convolutions allow improving results in terms of accuracy, the computational expenses may achieve dozens of GFLOPs. Another substantial drawback is that at some level of the network only a small number of weights inside the convolutional kernels have a significant impact on the output signal regarding their contribution to the absolute value of activations making utilization of resources ineffective. This problem was mentioned in @cite_43 @cite_37 where authors proposed decomposition techniques and mixed architectures that combine 3D and 2D operations on different levels of the network.
@cite_35 @cite_37 @cite_29 @cite_52 @cite_43 @cite_45
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In this paper we study 3D convolutional networks for video understanding tasks. Our starting point is the state-of-the-art I3D model, which "inflates" all the 2D filters of the Inception architecture to 3D. We first consider "deflating" the I3D model at various levels to understand the role of 3D convolutions. Interestingly, we found that 3D convolutions at the top layers of the network contribute more than 3D convolutions at the bottom layers, while also being computationally more efficient. This indicates that I3D is better at capturing high-level temporal patterns than low-level motion signals. We also consider replacing 3D convolutions with spatiotemporal-separable 3D convolutions (i.e., replacing convolution using a k * k * k filter with 1 * k * k followed by k * 1 * 1 filters); we show that such a model, which we call S3D, is 1.5x more computationally efficient (in terms of FLOPS) than I3D, and achieves better accuracy. Finally, we explore spatiotemporal feature gating on top of S3D. The resulting model, which we call S3D-G, outperforms the state-of-the-art I3D model by 3.5 accuracy on Kinetics and reduces the FLOPS by 34 . It also achieves a new state-of-the-art performance when transferred to other action classification (UCF-101 and HMDB-51) and detection (UCF-101 and JHMDB) datasets. We propose a new method for creating computationally efficient convolutional neural networks (CNNs) by using low-rank representations of convolutional filters. Rather than approximating filters in previously-trained networks with more efficient versions, we learn a set of small basis filters from scratch; during training, the network learns to combine these basis filters into more complex filters that are discriminative for image classification. To train such networks, a novel weight initialization scheme is used. This allows effective initialization of connection weights in convolutional layers composed of groups of differently-shaped filters. We validate our approach by applying it to several existing CNN architectures and training these networks from scratch using the CIFAR, ILSVRC and MIT Places datasets. Our results show similar or higher accuracy than conventional CNNs with much less compute. Applying our method to an improved version of VGG-11 network using global max-pooling, we achieve comparable validation accuracy using 41 less compute and only 24 of the original VGG-11 model parameters; another variant of our method gives a 1 percentage point increase in accuracy over our improved VGG-11 model, giving a top-5 center-crop validation accuracy of 89.7 while reducing computation by 16 relative to the original VGG-11 model. Applying our method to the GoogLeNet architecture for ILSVRC, we achieved comparable accuracy with 26 less compute and 41 fewer model parameters. Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46 less compute and 55 fewer parameters. Abstract Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to much manual design – the shape and size of the receptive field for convolutional operations is a very sensitive part that has to be tuned for different image analysis applications. 3D fully-convolutional multi-scale architectures with skip-connection that excel at semantic segmentation and landmark localisation have huge memory requirements and rely on large annotated datasets - an important limitation for wider adaptation in medical image analysis. We propose a novel and effective method based on trainable 3D convolution kernels that learns both filter coefficients and spatial filter offsets in a continuous space based on the principle of differentiable image interpolation first introduced for spatial transformer network. A deep network that incorporates this one binary extremely large and inflecting sparse kernel (OBELISK) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully-convolutional U-Net architectures on two challenging 3D CT multi-organ segmentation tasks. Extensive validation experiments indicate that the performance of sparse deformable convolutions is due to their ability to capture large spatial context with few expressive filter parameters and that network depth is not always necessary to learn complex shape and appearance features. A combination with conventional CNNs further improves the delineation of small organs with large shape variations and the fast inference time using flexible image sampling may offer new potential use cases for deep networks in computer-assisted, image-guided interventions. We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets, 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets, and 3) Our learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. In addition, the features are compact: achieving 52.8 accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute due to the fast inference of ConvNets. Finally, they are conceptually very simple and easy to train and use.