다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

A State-of-the-Art Survey on Deep Learning Theory and Architectures

A review on the attention mechanism of deep learning

https://ibug.doc.ic.ac.uk/media/uploads/documents/sandbach2012survey.pdf

Static and dynamic 3D facial expression recognition: A comprehensive survey

A survey on deep learning based face recognition

The invention relates to a hand motion identifying method and apparatus. The method comprises the following steps of acquiring a video to be identified; positioning and tracking a hand area in the video to be identified and extracting hand RGB video and depth information video pairs; detecting the hand RGB video and depth information video pairs to acquire feature points; adopting three-dimensional grid motion SIFT (scale-invariant feature transformation) feature descriptors to express the feature points; and comparing the three-dimensional grid motion SIFT feature descriptors of the feature points with three-dimensional grid motion SIFT feature descriptors acquired in advance by training and located in a positive class to acquire the class of hand motion in the video to be identified. The above-mentioned hand motion identifying method and apparatus extracting the feature points including the depth information make hand identifying accuracy greatly improved. By further adopting the three-dimensional grid motion SIFT feature descriptors to accurately express the feature points, the method and apparatus further improve the hand motion identifying accuracy.

Hand motion identifying method and apparatus

Visual object tracking is an important topic in computer vision, where the methods extracting features from the appearance of the object have made a significant progress. However, occlusion and rapid motion cause an incomplete appearance of the object and an incorrect search area in a complex scene, which limits the precision of object localization. In this paper, we propose a novel motion state prediction and localization network, named MP-LN, for visual object tracking, which predicts and translates a reasonable search area depending on the continuous motion state. Specially, we design a motion state prediction model based on the reinforcement learning, which adopts the policy gradients to estimate the target motion and incorporates rewards to enhance the back-propagation of errors for more accurate motion state. After that, we utilize an iterative localization to fine-tune the identification of the target location, reducing the response suppression. Extensive experiments and results demonstrate the effectiveness and advancement of the proposed method on six challenging tracking datasets, DTB70, UAVDT, UAV123, LaSOT, GOT-10k, and OTB2015.

MP-LN: motion state prediction and localization network for visual object tracking

Robust and accurate visual tracking is challenging as targets undergo significant changes in appearance by scale variance, occlusion and fast motion. We propose a novel tracking framework, called scalable spatiotemporal visual tracking algorithm (SSVT). First, we construct the Direction Prediction Model (DPM) to predict the spatiotemporal correlation of the target in the next frame. That will efficiently narrow down the search area and improve the accuracy of spatial location. Then, Occlusion Detection algorithm (ODA) is presented to overcome the wrong updates stemming from the region of interest (ROI) based on the estimated direction and Kalman filter. Finally, the multi-scale pyramid kernelized correlation filter (MSPKCF) is presented in tracking to realize the adaptive adjustment of the varying scales of the targets and the ROI size. Extensive experiments on OTB100 and VOT2016 datasets demonstrate that our tracker performs favorably against state-of-the-art trackers, which can effectively reduce computation redundancy and improve tracking accuracy.

Efficient scalable spatiotemporal visual tracking based on recurrent neural networks

Image captioning refers to automatic generation of descriptive texts according to the visual content of images. It is a technique integrating multiple disciplines including the computer vision (CV), natural language processing (NLP) and artificial intelligence. In recent years, substantial research efforts have been devoted to generate image caption with impressive progress. To summarize the recent advances in image captioning, we present a comprehensive review on image captioning, covering both traditional methods and recent deep learning-based techniques. Specifically, we first briefly review the early traditional works based on the retrieval and template. Then deep learning-based image captioning researches are focused, which is categorized into the encoder-decoder framework, attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction. After that, we summarize the publicly available datasets, evaluation metrics and those proposed for specific requirements, and then compare the state of the art methods on the MS COCO dataset. Finally, we provide some discussions on open challenges and future research directions.

Visuals to Text: A Comprehensive Review on Automatic Image Captioning

A novel system for 3D face recognition is presented in this paper. Firstly, we reduce the noise and move spikes from all the 3D faces. Secondly, we use Iterative Closet Point (ICP) to align all 3D face with the first person, and then for each face, we find the nose tip. Once the nose tip is successfully found, we crop a region, which is defined by a sphere radius of 100 mm centered at the nose tip. Depth image are constructed using the region subsequently. Then the depth image is projected into Gabor-based Supervised Locality Sensitive Discriminant Analysis (GISLSDA) space, which is improved by Gabor wavelet and Two-Directional Two Dimensions Principal Component Analysis (2D2PCA). Recognition is achieved by using a Nearest Neighbor (NN) classifier finally. This method is robust to changes in facial expressions and poses. The experimental results show that the new algorithm outperforms the other popular approaches reported in the literature and achieves much higher accurate recognition rate.

Yue Ming

Papers

Hand motion identifying method and apparatus

MP-LN: motion state prediction and localization network for visual object tracking

Efficient scalable spatiotemporal visual tracking based on recurrent neural networks

Visuals to Text: A Comprehensive Review on Automatic Image Captioning

A new scheme for 3D face recognition