Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is why not recycle off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating @math convolutions with @math convolutional filters on spatial domain (equivalent to 2D CNN) plus @math convolutions to construct temporal connections on adjacent feature maps in time. Furthermore, we propose a new architecture, named Pseudo-3D Residual Net (P3D ResNet), that exploits all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks. Our P3D ResNet achieves clear improvements on Sports-1M video classification dataset against 3D CNN and frame-based 2D CNN by 5.3 and 1.8 , respectively. We further examine the generalization performance of video representation produced by our pre-trained P3D ResNet on five different benchmarks and three different tasks, demonstrating superior performances over several state-of-the-art techniques.
The most recent approaches for video representation learning are to devise deep architectures. Karparthy stack CNN-based frame-level representations in a fixed size of windows and then leverage spatio-temporal convolutions for learning video representation @cite_33 . In @cite_36 , the famous two-stream architecture is devised by applying two CNN architectures separately on visual frames and staked optical flows. This architecture is further extended by exploiting multi-granular structure @cite_14 @cite_7 @cite_16 , convolutional fusion @cite_18 , key-volume mining @cite_25 and temporal segment networks @cite_8 for video representation learning. In the work by @cite_6 , the local ConvNet responses over the spatio-temporal tubes centered at the trajectories are pooled as the video descriptors. Fisher Vector @cite_12 is then used to encode these local descriptors to a global video representation. Recently, the LSTM-RNN networks have been successfully employed for modeling temporal dynamics in videos. In @cite_5 @cite_29 , temporal pooling and stacked LSTM network are leveraged to combine frame-level (optical flow images) representation and discover long-term temporal relationships for learning a more robust video representation. Srivastava @cite_21 further formulate the video representation learning task as an autoencoder model based on the encoder and decoder LSTMs.
@cite_18 @cite_14 @cite_33 @cite_7 @cite_8 @cite_36 @cite_29 @cite_21 @cite_6 @cite_5 @cite_16 @cite_25 @cite_12
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3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 @math 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion , which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets). This paper presents a novel method to involve both spatial and temporal features for semantic video segmentation. Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task. We investigate how involving temporal features also has a good effect on segmenting video data. We propose a module based on a long short-term memory (LSTM) architecture of a recurrent neural network for interpreting the temporal characteristics of video frames over time. Our system takes as input frames of a video and produces a correspondingly-sized output; for segmenting the video our method combines the use of three components: First, the regional spatial features of frames are extracted using a CNN; then, using LSTM the temporal features are added; finally, by deconvolving the spatio-temporal features we produce pixel-wise predictions. Our key insight is to build spatio-temporal convolutional networks (spatio-temporal CNNs) that have an end-to-end architecture for semantic video segmentation. We adapted fully some known convolutional network architectures (such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets. Generating natural language descriptions for in-the-wild videos is a challenging task. Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video. However, these deep CNN architectures are designed for single-label centered-positioned object classification. While they generate strong semantic features, they have no inherent structure allowing them to detect multiple objects of different sizes and locations in the frame. Our paper tries to solve this problem by integrating the base CNN into several fully convolutional neural networks (FCNs) to form a multi-scale network that handles multiple receptive field sizes in the original image. FCNs, previously applied to image segmentation, can generate class heat-maps efficiently compared to sliding window mechanisms, and can easily handle multiple scales. To further handle the ambiguity over multiple objects and locations, we incorporate the Multiple Instance Learning mechanism (MIL) to consider objects in different positions and at different scales simultaneously. We integrate our multi-scale multi-instance architecture with a sequence-to-sequence recurrent neural network to generate sentence descriptions based on the visual representation. Ours is the first end-to-end trainable architecture that is capable of multi-scale region processing. Evaluation on a Youtube video dataset shows the advantage of our approach compared to the original single-scale whole frame CNN model. Our flexible and efficient architecture can potentially be extended to support other video processing tasks. In this paper, we propose a novel two-stream framework based on combinational deep neural networks. The framework is mainly composed of two components: one is a parallel two-stream encoding component which learns video encoding from multiple sources using 3D convolutional neural networks and the other is a long-short-term-memory (LSTM)-based decoding language model which transfers the input encoded video representations to text descriptions. The merits of our proposed model are: 1) It extracts both temporal and spatial features by exploring the usage of 3D convolutional networks on both raw RGB frames and motion history images. 2) Our model can dynamically tune the weights of different feature channels since the network is trained end-to-end from learning combinational encoding of multiple features to LSTM-based language model. Our model is evaluated on three public video description datasets: one YouTube clips dataset (Microsoft Video Description Corpus) and two large movie description datasets (MPII Corpus and Montreal Video Annotation Dataset) and achieves comparable or better performance than the state-of-the-art approaches in video caption generation.