22 citations · 53 across the 6 of their papers we have counts for
6 papers · 1 filter
Collaborative Method for Incremental Learning on Classification and Generation
Byungju Kim, Jaeyoung Lee, Kyungsu Kim +2
Although well-trained deep neural networks have shown remarkable performance on numerous tasks, they rapidly forget what they have learned as soon as they begin to learn with addit…
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
Yunjae Jung, Dahun Kim, Sanghyun Woo +3
Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but a…
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering
Junyeong Kim, Minuk Ma, Kyungsu Kim +2
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering. Multi-modal video question answering is an important task t…
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation
Xiaobing Wang, Yingying Jiang, Zhenbo Luo +3
Scene text detection attracts much attention in computer vision, because it can be widely used in many applications such as real-time text translation, automatic information entry,…
Progressive Attention Memory Network for Movie Story Question Answering
Junyeong Kim, Minuk Ma, Kyungsu Kim +2
This paper proposes the progressive attention memory network (PAMN) for movie story question answering (QA). Movie story QA is challenging compared to VQA in two aspects: (1) pinpo…
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim +2
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distrib…