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

Chorioamnionitis and Risk for Maternal and Neonatal Sepsis: A Systematic Review and Meta-analysis.

Synthesizing realistic data samples is of great value for both academic and industrial communities. Deep generative models have become an emerging topic in various research areas like computer vision and signal processing. Affective computing, a topic of a broad interest in computer vision society, has been no exception and has benefited from generative models. In fact, affective computing observed a rapid derivation of generative models during the last two decades. Applications of such models include but are not limited to emotion recognition and classification, unimodal emotion synthesis, and cross-modal emotion synthesis. As a result, we conducted a review of recent advances in human emotion synthesis by studying available databases, advantages, and disadvantages of the generative models along with the related training strategies considering two principal human communication modalities, namely audio and video. In this context, facial expression synthesis, speech emotion synthesis, and the audio-visual (cross-modal) emotion synthesis is reviewed extensively under different application scenarios. Gradually, we discuss open research problems to push the boundaries of this research area for future works.

Generative Adversarial Networks in Human Emotion Synthesis:A Review

Deep generative models have become an emerging topic in various research areas like computer vision and signal processing. These models allow synthesizing realistic data samples that are of great value for both academic and industrial communities. Affective computing, a topic of a broad interest in computer vision society, has been no exception and has benefited from this powerful approach. In fact, affective computing observed a rapid derivation of generative models during the last two decades. Applications of such models include but are not limited to emotion recognition and classification, unimodal emotion synthesis, and cross-modal emotion synthesis. As a result, we conducted a comprehensive survey of recent advances in human emotion synthesis by studying available databases, advantages, and disadvantages of the generative models along with the related training strategies considering two principal human communication modalities, namely audio and video. In this context, facial expression synthesis, speech emotion synthesis, and the audio-visual (cross-modal) emotion synthesis are reviewed extensively under different application scenarios. Gradually, we discuss open research problems to push the boundaries of this research area for future works. As conclusions, we indicate common problems that can be explored from the Generative Adversarial Networks (GAN) topologies and applications in emotion synthesis.

Generative Adversarial Networks in Human Emotion Synthesis: A Review

Pathologic Assessment of the Placenta

AI-PLAX: AI-based placental assessment and examination using photos.

Multi-region saliency-aware learning for cross-domain placenta image segmentation

A significant proportion of individuals' daily activities is experienced through digital devices. Smartphones in particular have become one of the preferred interfaces for content consumption and social interaction. Identifying the content embedded in frequently-captured smartphone screenshots is thus a crucial prerequisite to studies of media behavior and health intervention planning that analyze activity interplay and content switching over time. Screenshot images can depict heterogeneous contents and applications, making the a priori definition of adequate taxonomies a cumbersome task, even for humans. Privacy protection of the sensitive data captured on screens means the costs associated with manual annotation are large, as the effort cannot be crowd-sourced. Thus, there is need to examine utility of unsupervised and semi-supervised methods for digital screenshot classification. This work introduces the implications of applying clustering on large screenshot sets when only a limited amount of labels is available. In this paper we develop a framework for combining K-Means clustering with Active Learning for efficient leveraging of labeled and unlabeled samples, with the goal of discovering latent classes and describing a large collection of screenshot data. We tested whether SVM-embedded or XGBoost-embedded solutions for class probability propagation provide for more well-formed cluster configurations. Visual and textual vector representations of the screenshot images are derived and combined to assess the relative contribution of multi-modal features to the overall performance.

Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning.

Recent advancement of Generative Adversarial Network (GAN) based architectures has achieved impressive performance on static facial expression synthesis. Continuous affect synthesis, which has applications in generating videos and movies, is underexplored. Synthesizing continuous photo-realistic facial emotion expressions from a static image is challenging because a) there is a lack of consensus on what parameters produce a smooth shift of facial expressions and b) it is difficult to balance between the consistency over personalized features and granularity among continuous emotional states. We adapt one of the most successful networks, StarGAN, and propose StarGAN-EgVA to generate continuous facial emotions based on 2D emotional representations, i.e., valence and arousal (VA). We propose to utilize categorical emotions ( e.g., happy, sad) to guide the regression training on VA intensities so that the model learns both the domain-specific features and subtle changes introduced by different VA intensities. A special trick at testing is also exploited to automatically infer emotion labels from the VA point on a 2D emotional plane to ensure smooth transition among emotional states. Qualitative and quantitative experiments demonstrate our proposed model's ability to generate more photo-realistic and consistent affect sequences than the state-of-the-art.

StarGAN-EgVA: Emotion Guided Continuous Affect Synthesis

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1101&context=pjsl

Dolzodmaa Davaasuren

Papers

AI-PLAX: AI-based placental assessment and examination using photos.

Multi-region saliency-aware learning for cross-domain placenta image segmentation

Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning.

StarGAN-EgVA: Emotion Guided Continuous Affect Synthesis

Smart Health Monitoring System: A Human-Centered Design