KAIST EE Presented Cutting-edge AI Research Results At CVPR 2019

CVPR is the premier computer vision conference in the world. The quality and impact of instituion's research activity in computer vision and artificial intelligence are often measured by the number of papers accepted to this conference. KAIST EE has been very prolific in this sense. At 2019 CVPR alone, KAIST EE researchers have published 12 papers, becoming one of the most productive institutions of the world in computer vision and artificial intelligence research. These papers can be found below:

 

Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence

Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon


Deep Video Inpainting

Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon


Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning

Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, In So Kweon


Learning Loss for Active Learning

Donggeun Yoo, In So Kweon


Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images

Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon


dge-Labeling Graph Neural Network for Few-shot Learning 

Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo


Progressive Attention Memory Network for Movie Story Question Answering

Junyeong Kim, Minuk Ma, Kyungsu Kim, Sungjin Kim, Chang D. Yoo


Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection 

Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, Changick Kim


Learning Not to Learn: Training Deep Neural Networks with Biased Data

Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, Junmo Kim


RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion 

Muhammad Sarmad, Hyunjoo Jenny Lee, Young Min Kim


Efficient Neural Network Compression 

Hyeji Kim, Muhammad Umar Karim Khan, Chong-Min Kyung


Variational Information Distillation for Knowledge Transfer 

Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai

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