“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Under the guidance of the material genome initiative (MGI), the use of data-driven methods to discover new materials has become an innovation of materials science. The polymer materials have been one of the most important parts in materials science for the excellent physical and chemical properties as well as corresponding complex structures. Machine learning, as the core of data-driven methods, has taken an important place in polymer materials design and discovery. In this review, the authors have introduced the applications of machine learning in the design and discovery of polymer materials. The development tendency of published papers about machine learning in polymer materials, the commonly used algorithms, the polymer descriptors, the workflow of machine learning in polymer materials, and recent progresses of machine learning in materials are summarized. Then, the detail of how to use machine learning to assist design and discovery of polymer materials is fully discussed combined with two cases. Finally, the opportunities and challenges on the future development prospects of machine learning in the field of polymer materials are proposed. 

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.

/pdf/skeleton-graph-neural-network-based-human-action-recognition-22pzo89b.pdf

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Deep residual convolutional neural network based on hybrid attention mechanism for ecological monitoring of marine fishery

This article designs a visual surveillance framework for human fall detection. In order to solve the conventional issues in fall detection, such as unsatisfactory feature generalization, low recall rates, and large computational time, we design a model that incorporates the deep convolutional neural network and the aggregated heuristic visual features in detecting the occurrence of falls. First, the convolutional neural network (Openpose model) is utilized to extract human skeleton in the image. Second, the hand-crafted spatial features, such as the angle of human shank inclination, are aggregated to determine the fall presence. It should be noticed that our fall detection method has been integrated to healthcare Internet of Things (IoT) video surveillance architecture, which has multiple graphic processing unit groups to perform real-time monitoring and alarming for the elderly in need. The experimental results prove that our method is able to accurately distinguish fall and nonfall activities with a competitive false-alarm rate.

Visual Surveillance for Human Fall Detection in Healthcare IoT

Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article, regarding skeleton data as a directed graph, we design directed graph convolutional neural networks with a novel residual split structure. First, we construct a directed graph represent model to extract human behavior by two kinds of graph models. Second, we use a novel residual split block to construct graph convolution neural network. Different from the traditional residual networks, we split high-dimensional features into several shallow features with the same dimension. It can not only ensure the diversity of mining features, but also avoid the gradient disappearing. Finally, during training, we use random sampling of data to reduce the burden of network training. Experiment results show that the proposed method achieves higher recognition rate than the comparative methods on the NTU-RGBD dataset.

Deep Residual Split Directed Graph Convolutional Neural Networks for Action Recognition

Double enhanced residual network for biological image denoising.

Single image super-resolution (SISR) is an image processing task which obtains high-resolution (HR) image from a low-resolution (LR) image. Recently, due to the capability in feature extraction, a series of deep learning methods have brought important crucial improvement for SISR. However, we observe that no matter how deeper the networks are designed, they usually do not have good generalization ability, which leads to the fact that almost all of existing SR methods have poor performances on restoration of the weak texture details. To solve these problems, we propose a weak texture information map guided image super-resolution with deep residual networks. It contains three sub-networks, one main network which extracts the main features and fuses weak texture details, another two auxiliary networks extract the weak texture details fallen in the main network. Two part of networks work cooperatively, the auxiliary networks predict and integrates week texture information into the main network, which is conducive to the main network learning more inconspicuous details. Experiments results demonstrate that our method's performs achieve the state-of-the-art quantitatively. Specifically, the image super-resolution results of our method own more weak texture details.

Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks

Limited by the poor quality of the camera, transmission bandwidth, excessive compression and other factors, low-resolution images widely exist in our lives. Single image super-resolution method is a kind of image processing task which can obtain high-resolution image from corresponding source low-resolution image. With the development of deep learning technology, a series of deep learning methods have brought crucial improvement for SISR problem. However, we observe that no matter how deep the network structures for SISR are designed, they usually have poor performances on restoration of tiny or irregular details, we call them weak texture information. The main reason for this phenomenon is that weak texture information is not obvious relative to the salient features, so as weak texture information feature is less extracted in the process of neural network feature extraction. To address this problem, we propose a SISR method which owns unique weak texture information prediction module and call it as weak texture information map guided image super-resolution with deep residual networks. In our network structure, two auxiliary sub-networks work together to capture grab details information and predict weak texture information. Then, predicted weak texture information will be fused into main sub-network. Therefore, our network can obtain more irregular details which usually miss in deep learning based methods. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm. Specifically, our method can restore more irregular image details.

Week Texture Information Map Guided Image Super-resolution with Deep Residual Networks

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme. The DFSA and DFN respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to facilitate better restoration. In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training scheme. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on $3$ tasks, including image deraining, image deblurring, and real image denoising. Code is available at https://github.com/wlydlut/C2F-DFT.

pdf/learning-a-coarse-to-fine-diffusion-transformer-for-image-293r8qsnyf.pdf

Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

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