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Tag: Deep Learning
  • Research Highlight

    Artificial Intelligence to discover hidden enzymes

    A joint research team composed of members from KAIST and UCSD developed an artificial intelligence that predicts enzyme functions from protein sequences....read more

    AI Deep Learning Enzyme KI for the BioCentury and KI for Artificial Intelligence Machine Learning Metabolic and Biomolecular Engineering National Research Laboratory (MBEL) Metabolic Engineering Metabolism
  • Research Highlight

    SphereSR: 360º Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation

    A new algorithm has been developed to generate omnidirectional images to super-resolve a low-resolution 360° image into a high-resolution image with arbitrary projection type via continuous spherical image representation....read more

    Computer Vision Deep Learning KAIST Institute for Robotics Machine Learning Super-resolution Super-resolution with 360° Camera
  • Research Highlight

    Imaging with Events: High-resolution High-quality HDR Imaging using Event Cameras

    New algorithms have been developed to generate non-blurry, high-resolution, high-quality, and high-dynamic range intensity images using sparse event streams from an event camera, which is a new imaging sensor able to capture visual information with low latency even under extremely low illumination....read more

    AI for Cooperative Robots Computer Vision Deep Learning Event Camera KI for Robotics (KIR) Machine Learning Super-resolution Super-resolution with Event Camera
  • Research Highlight

    Development of Machine Reading Framework for Knowledge Base Population

    The SWRC (Semantic Web Research Center, led by Prof. Choi) aims to construct and expand its knowledge base through self-machine-learning from unstructured big data (natural language), and to develop a novel technology to further verify the knowledge base....read more

    AI Applications Deep Learning Information Extraction KI for Artificial Intelligence Knowledge Base
  • Research Highlight

    Self-Supervised Learning to Distill Hierarchy in High-Dimensional Dynamic Systems

    Prof. Han-Lim Choi’s research team has developed a learning framework to control a high-dimensional robotic system that distills underlying hierarchical structure in robot motion data. By learning high-level intentions as well as low-level control actions, the proposed framework enables adaptation of policy learned from a certain task to much more diverse sets of tasks. ...read more

    AI for Cooperative Robots Deep Learning High-Dimensional Systems Interpretable AI KI for Robotics Reinforcement Learning Representation Learning Robust Intelligence Under Uncertainty
  • Research Highlight Top Story

    Development of VR Sickness Assessment Deep Network Considering Exceptional Motion for VR Video

    Viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness can occur when watching immersive VR content. To deal with viewing safety for VR content, objective assessment of VR sickness is of great importance. In this work, based on a deep generative model, we propose a novel objective VR sickness assessment (VRSA) network to automatically predict VR sickness. The proposed method takes into account motion patterns of VR videos in which exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, the VR video generator and VR sickness score predictor. For the evaluation of VRSA performance, we performed comprehensive experiments with 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires. We demonstrated that the proposed VRSA achieved a high correlation with human perceptual score for VR sickness....read more

    360-degree Video AI Applications Deep Learning Human Perception Image and Video sYstems (IVY) Laboratory KI for Artificial Intelligence Motion Mismatch Objective Assessment Virtual Reality (VR) VR Sickness
  • Research Highlight

    Explainable AI can predict prognosis-specific genotype of brain tumor without invasive biopsy

    Professor Bumseok Jeong has developed an explainable artificial intelligence model with high diagnostic performance that predicts the IDH genotype of gliomas; this is crucial in treatment planning and prognosis prediction....read more

    Brain Imaging and Neuromodulation Computational Neuroscience Deep Learning KI for Health Science and Technology Laboratory of Computational Affective Neuroscience and Development Neuroimaging and Neuromodulation
  • Research Highlight

    3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

    Professor Jong-Hwan Kim’s research team defined a 3-D scene graph, which represents physical environments in a sparse and semantic way....read more

    3-D Scene Graph AI AI Applications ASIT RITL (Robot Intelligent Technology Lab) Deep Learning Environment Model Human Robot Interaction Intelligent Agent KI for Artificial Intelligence Robot Intelligence Scene Graph Scene Understanding
  • Research Highlight

    Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information

    We confirmed which combination of features of multi-modal emotion recognition achieves the highest accuracy....read more

    AI Applications Deep Learning Facial Expression Recognition HRI Human Robot Interaction KAIST Institute for Artificial Intelligence
  • Research Highlight

    Research and Development of AI-powered Autonomous Cars in Seoul for Smart Cities

    Professor Hyunchul Shim’s automotive driving research team participated in a technology demonstration organized by the Ministry of Land, Infrastructure and Transport, Korea. The event was held on Yeongdong boulevard adjacent to COEX, Seoul on June 17th. ...read more

    Autonomous driving Deep Learning USRG(Unmanned System Research Group)

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