Graph Convolution Networks (GCNs) have become the main-stream framework for skeleton-based action recognition tasks. Aiming at the problem of redundant spatial-temporal feature information and neighborhood constraints obtained in GCNs, we propose a novel method called Multi-Attention Network (MA-Net) to explore crucial skeleton information, including two main modules: Combined Attention Graph Convolution (CAGC) and Multi-layer Transposed Attention Encoding (MTAE). The CAGC utilizes multi-dimensional combination attention to capture more valuable information and enhance feature performance. The MTAE adopts self-attention to encode feature maps, effectively establishing long-range dependency and capturing global information. Centre on the attention mechanism, these two modules combine the complementary advantages of GCN (i.e., local topology and temporal dynamics) and Transformer (i.e., global context and dynamic attention). Extensive experiments on the challenging NTU-RGB+D 60 and Kinetics-Skeleton datasets demonstrate that our model performs excellently.

Recently, neural network-based image compression techniques have demonstrated remarkable compression performance. The use of context-adaptive entropy models greatly enhances the rate-distortion (R-D) performance by effectively capturing spatial redundancy in latent representations. However, latent representations still contain some spatial correlations(e.g. same spatial structure), it needs to be eliminated by further processing. And many compression models are single-rate model, which is difficult to cover a big range of bitrate. In order to address this issue, we propose a novel variable-rate image compression algorithm that efficiently leverages bi-resolution spatial-channel information through learned mechanisms. In this paper, we first proposed a BRP network to divide our latent representations and side information into HR and LR components, eliminating the spatial redundancy in same location. Combining the spatial-channel context, we proposed a BSC context model, including a decreasing-granularity checkerboard pattern and channel grouping based on cosine slicing strategy. To cover a wide range of bitrate, we take a weight map as input to control bit allocation, achieving multiple compression rates. Our experimental results show that our method provides a better rate-distortion trade-off than BPG, JPEG and other recent image compression methods based on deep learning.

In recent years, there have been a growing interest in developing end-to-end neural video codecs. Previous works generally use a past decoded frame as reference directly, utilizing the motion information between it and the input frame to reduce temporal redundancy. However, this approach may lead to high bit rate consumption of the motion and fails to take advantage of the prior information in other reconstructed frames. In this work, We propose a learned video coding framework with optical flow based feature prediction module and decomposed context module. Specifically, we employ the previous optical flow to generate a warped frame, and along with other reconstructions, they are used for a more accurate reference forecasting, thereby reducing the bit rate required for motion compression. Moreover, based on the conditional coding framework, our decomposed context module explores conditional context in past decoded frames and further reduces additional spatiotemporal correlations. Experimental results demonstrate that our approach yields better performance than previous learned video compression methods and traditional standard codecs. For example, our neural codec achieves 28.94% coding gain over HEVC in PSNR metric and about 2.00% coding gain over VVC in MS-SSIM metric.

Recently, human action recognition has been widely used in the fields of health monitoring, human-robot interaction, medical treatment, and sports. Due to the availability of various wearable devices on the market, we can easily access sensor data for human action recognition. However, it is still a challenge to capture minute action processes as well as extract spatio-temporal motion patterns from serial sensor data. Therefore, we propose a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The finer-grained spatial information in the sensor signals is extracted by hierarchical multi-scale convolution. The multi-channel feature fusion through adaptive channel feature fusion retains important information and improves model efficiency. We capture temporal context information by dynamic channel selection-LSTM based on the attention mechanism. Extensive experiments on the Opportunity and PAMAP2 public datasets show that our proposed model achieves competitive performance compared to several state-of-the-art approaches.

With the development of multimedia, video surveillance systems are becoming more popular. However, the current video surveillance systems have a general function and are unable to provide Intelligent perception.

Human action recognition is an active research area in computer vision. Aiming at the lack of spatial muti-scale information for human action recognition, we present a novel framework to recognize human actions from depth video sequences using multi-scale Laplacian pyramid depth motion images (LP-DMI). Each depth frame is projected onto three orthogonal Cartesian planes. Under three views, we generate depth motion images (DMI) and construct Laplacian pyramids as structured multi-scale feature maps which enhances multi-scale dynamic information of motions and reduces redundant static information in human bodies. We further extract the multi-granularity descriptor called LP-DMI-HOG to provide more discriminative features. Finally, we utilize extreme learning machine (ELM) for action classification. Through extensive experiments on the public MSRAction3D datasets, we prove that our method outperforms state-of-the-art benchmarks.