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

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

The PASCAL Visual Object Classes Challenge

A morphable model for the synthesis of 3D faces

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

/pdf/multimodal-fusion-for-multimedia-analysis-a-survey-5f9wxprbby.pdf

Multimodal fusion for multimedia analysis: a survey

From past to present, individuals' appreciation of taste has always been wondered. Moreover, there is an increasing research interest in measuring taste appreciation. Most of the previous work in this area are psychological studies that rely on manual coding of facial actions and/or emotional expressions. Consequently, these studies depend on human observations. We propose a preliminary study for an automatic visual analysis system that estimates taste liking of individuals. Our results show that the proposed system performs with 56.6% accuracy to classify appreciation in terms of liking, neutral, and disliking categories. In order to explore this result in detail, classification of liking level pairs such as disliking-vs-liking, neutral-vs-liking, and neutral-vs-disliking are also evaluated. Our system achieved 72.5% accuracy for distinguishing between dislike and liking, however, classification accuracy for dislike and neutral, and for liking and neutral have been found to be lower. Our results suggest that reliable and fast automatic systems can be developed to estimate taste appreciation, yet classes of liking and neutral state are not easily separable as indicated in previous studies.

A preliminary study on visual estimation of taste appreciation

In this paper, using a general convolutional neural network (CNN) model, which was developed for object recognition, a successful system has been introduced for the person re-identification problem. To use this CNN model for the person re-identification problem properly, it is individually fine-tuned using different body parts of person images. For feature extraction, we used the seventh layer of the CNN model, which was re-trained with the available datasets. Then, we used cosine similarity metric to calculate the similarity between extracted features. CUHK03 and Market-1501 datasets were used as the training sets and the proposed method has been tested on VIPeR dataset. Superior results have been obtained with the proposed method, compared to the state-of-the-art methods in the field.

Convolutional neural network-based representation for person re-identification

Reinforcement learning began to perform at human-level success in game intelligence after deep learning revolution. Geometry Friends is a puzzle game, where we can benefit from deep learning and expect to have successful game playing agents. In the game, agents are collecting targets in two dimensional environment and they try to overcome obstacles in the way. In this paper, Q-learning approach is applied to this game and a generalized circle agent for different types of environment is implemented. Agent is trained by giving only screen pixels as input via a Convolutional Neural Network. Experimental results show that with the proposed method game completion rate and completion times are improved compared to random agent.

Generalized circle agent for geometry friends using deep reinforcement learning

Investigation into robot-assisted rehabilitation systems, and robot-assisted systems that are capable of detecting patient's emotions and then modifying the rehabilitation task to better suit the patients' abilities by taking account their emotions have gained momentum in recent years. In this paper, our aim is to distinguish whether the subject is under-challenged or over-challenged using psychophysiological signal data collected from biofeedback sensors while executing the tasks with RehabRoby. Initially, features are extracted from the physiological signals (Blood Volume Pulse (BVP), Skin Conductance (SC), and Skin Temperature (ST)). The extracted features are examined in terms of their contribution to the classification of the overstressed/over-challenged, boredom/under-challenged using variance analysis (ANOVA). The most significant features are selected, and various classification methods are used to classify overstressed/over-challenged, boredom/under-challenged.

Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Visual speech recognition is a challenging research problem with a particular practical application of aiding audio speech recognition in noisy scenarios. Multiple camera setups can be beneficial for the visual speech recognition systems in terms of improved performance and robustness. In this paper, we explore this aspect and provide a comprehensive study on combining multiple views for visual speech recognition. The thorough analysis covers fusion of all possible view angle combinations both at feature level and decision level. The employed visual speech recognition system in this study extracts features through a PCA-based convolutional neural network, followed by an LSTM network. Finally, these features are processed in a tandem system, being fed into a GMM-HMM scheme. The decision fusion acts after this point by combining the Viterbi path log-likelihoods. The results show that the complementary information contained in recordings from different view angles improves the results significantly. For example, the sentence correctness on the test set is increased from 76% for the highest performing single view (30 degrees) to up to 83% when combining this view with the frontal and (60 degrees) view angles

https://avsp2017.loria.fr/wp-content/uploads/2017/07/AVSP2017_paper_25.pdf

Hazim Kemal Ekenel

Papers

A preliminary study on visual estimation of taste appreciation

Convolutional neural network-based representation for person re-identification

Generalized circle agent for geometry friends using deep reinforcement learning

Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Combining Multiple Views for Visual Speech Recognition