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

Individual teaching has been considered as the most successful educational form since ancient times. This form still continues its existence nowadays within intelligent systems intended to provide adapted tutoring for each student. Although, recent research has shown that emotions can affect student's learning, adaptation skills of tutoring systems are still imperfect due to weak emotional intelligence. To enhance ongoing research related to the improvement of the tutoring adaptation based on both student's knowledge and emotional state, the paper presents an analysis of emotion recognition methods used in recent developments. Study reveals that sensor-lite approach can serve as a solution to problems related to emotion identification accuracy. To provide ground-truth data for emotional state, we have explored and implemented a selfassessment method.

/pdf/emotion-recognition-for-intelligent-tutoring-4a18pvm87g.pdf

Emotion Recognition for Intelligent Tutoring

Facial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks. We propose two evaluation protocols for this dataset. Benchmark experiments on the dataset have shown that no matter how powerful the deep face representation models are, their performance degrades significantly when they are tested on real-world occluded faces. It is observed that the performance drop is far less when the models are tested on synthetically generated occluded faces. The ROF dataset and the associated evaluation protocols are publicly available at the following link https://github.com/ekremerakin/RealWorldOccludedFaces.

On Recognizing Occluded Faces in the Wild

We describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed. Index Terms: speaker identification, face recognition, named entity detection, video optical character recognition, multimodal fusion

QCompere @ REPERE 2013

Varying illumination conditions cause a dramatic change in facial appearance that leads to a significant drop in face recognition algorithms' performance. In this paper, to overcome this problem, we utilize an automatic frequency band selection scheme. The proposed approach is incorporated to a local appearance-based face recognition algorithm, which employs discrete cosine transform (DCT) for processing local facial regions. From the extracted DCT coefficients, the approach determines to the ones that should be used for classification. Extensive experiments conducted on the extended Yale face database B have shown that benefiting from frequency information provides robust face recognition under changing illumination conditions.

https://publikationen.bibliothek.kit.edu/1000026383/2023963

Automatic Frequency Band Selection for Illumination Robust Face Recognition

Automatic emotion recognition in the wild video datasets is a very challenging problem because of the inter-class similarities among different facial expressions and large intra-class variabilities due to the significant changes in illumination, pose, scene, and expression. In this paper, we present our proposed method for video-based emotion recognition in the EmotiW 2016 challenge. The task considers the unconstrained emotion recognition problem by training from short video clips extracted from movies and testing on short movie clips and spontaneous video clips of the reality TV data. Four different methods are employed to extract both static and dynamic emotion representations from the videos. First, local binary patterns of three orthogonal planes are used to describe spatiotemporal features of the video frames. Second, principal component analysis is applied to the image patches in a two-stage convolutional network to learn weights and extract facial features from the aligned faces. Third, the deep convolutional neural network model of VGG-Face is deployed to extract deep facial representations from aligned faces. Fourth, a bag of visual words is computed based on dense scale-invariant feature transform descriptors from aligned face images to form hand-crafted representations. Support vector machines are then utilized to train and classify the obtained spatiotemporal representations and facial features. Finally, score-level fusion is applied to combine the classification results and predict the emotion labels of the video clips. The results show that the proposed combined method has outperformed all the utilized techniques with the overall validations and test accuracies of 43.13% and 40.13%, respectively. This system, is relatively a good classifier in Happy and Angry emotion categories and is unsuccessful in detecting Surprise, Disgust, and Fear.

Hazim Kemal Ekenel

Papers

Emotion Recognition for Intelligent Tutoring

On Recognizing Occluded Faces in the Wild

QCompere @ REPERE 2013

Automatic Frequency Band Selection for Illumination Robust Face Recognition

Automatic emotion recognition in the wild using an ensemble of static and dynamic representations