no code implementations • 29 Oct 2020 • Byungju Kim, Jaeyoung Lee, KyungSu Kim, Sungjin Kim, Junmo Kim
In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks.
no code implementations • 3 Feb 2020 • Yunjae Jung, Dahun Kim, Sanghyun Woo, Kyung-Su Kim, Sungjin Kim, In So Kweon
In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap.
Ranked #7 on Visual Storytelling on VIST
no code implementations • 28 May 2019 • Junyeong Kim, Minuk Ma, Kyung-Su Kim, Sungjin Kim, Chang D. Yoo
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering.
no code implementations • CVPR 2019 • Xiaobing Wang, Yingying Jiang, Zhenbo Luo, Cheng-Lin Liu, Hyun-Soo Choi, Sungjin Kim
Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found.
no code implementations • CVPR 2019 • Junyeong Kim, Minuk Ma, Kyung-Su Kim, Sungjin Kim, Chang D. Yoo
To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer.
Ranked #2 on Video Story QA on MovieQA
4 code implementations • CVPR 2019 • Byungju Kim, Hyunwoo Kim, Kyung-Su Kim, Sungjin Kim, Junmo Kim
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased.
no code implementations • ECCV 2018 • Sunghun Kang, Junyeong Kim, Hyun-Soo Choi, Sungjin Kim, Chang D. Yoo
The architecture is trained to maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific stream in the network.