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bio yutong dai dr yutong dai dr publications yutong dai researcher follow researchgate linkedin github google scholar bio i obtained my phd degree at school of computer science the university of adelaide under the supervision of prof chunhua shen my major research interests are about computer vision with its application on dense prediction and data effcient learning email yutong dot dai2023 at outlook dot com education ph d the university of adelaide australia 2018 2022 m s southeast university china 2015 2018 b s nanjing normal university china 2011 2015 experience postdoctoral researcher australian institute for machine learning aiml 2022 2023 research intern adobe research 2021 student tutor university of adelaide 2019 2021 teaching assistant southeast university 2015 publications indices matter learning to index for deep image matting hao lu yutong dai chunhua shen songceng xu 2019 indices matter learning to index for deep image matting iccv 2019 index networks hao lu yutong dai chunhua shen songceng xu 2020 index networks tpami 2020 towards light weight deep portrait matting via parameter sharing yutong dai hao lu chunhua shen 2021 towards light weight deep portrait matting via parameter sharing cgf 2021 learning affinity aware upsampling for deep image matting yutong dai hao lu chunhua shen 2021 learning affinity aware upsampling for deep image matting cvpr 2021 boosing robustness of image matting with context assembing and strong data augmentation yutong dai brian price he zhang chunhua shen 2022 boosing robustness of image matting with context assembing and strong data augmentation cvpr 2022 infusing definiteness into randomness rethinking composition styles for deep image matting zixuan ye yutong dai chaoyi hong zhiguo cao hao lu 2023 infusing definiteness into randomness rethinking composition styles for deep image matting aaai 2023 services reviewer for cvpr 21 22 23 iccv 21 eccv 22 aaai 22 23 acmmm 23 acml 23 tmm tpami tcsvt ijcv sitemap follow github feed 2023 yutong dai powered by jekyll academicpages a fork of minimal mistakes
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