Search Results for author: Kyoung-Woon On

Found 12 papers, 3 papers with code

Video-Text Representation Learning via Differentiable Weak Temporal Alignment

1 code implementation CVPR 2022 Dohwan Ko, Joonmyung Choi, Juyeon Ko, Shinyeong Noh, Kyoung-Woon On, Eun-Sol Kim, Hyunwoo J. Kim

In this paper, we propose a novel multi-modal self-supervised framework Video-Text Temporally Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant information from noisy and weakly correlated data using a variant of Dynamic Time Warping (DTW).

Contrastive Learning Dynamic Time Warping +1

Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts

no code implementations13 Oct 2021 Minchul Shin, Jonghwan Mun, Kyoung-Woon On, Woo-Young Kang, Gunsoo Han, Eun-Sol Kim

The VALUE (Video-And-Language Understanding Evaluation) benchmark is newly introduced to evaluate and analyze multi-modal representation learning algorithms on three video-and-language tasks: Retrieval, QA, and Captioning.

Representation Learning Transfer Learning

Spectrally Similar Graph Pooling

no code implementations1 Jan 2021 Kyoung-Woon On, Eun-Sol Kim, Il-Jae Kwon, Sangwoong Yoon, Byoung-Tak Zhang

To further investigate the effectiveness of our proposed method, we evaluate our approach on a real-world problem, image retrieval with visual scene graphs.

Image Retrieval

DramaQA: Character-Centered Video Story Understanding with Hierarchical QA

1 code implementation7 May 2020 Seong-Ho Choi, Kyoung-Woon On, Yu-Jung Heo, Ahjeong Seo, Youwon Jang, Minsu Lee, Byoung-Tak Zhang

Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic difficulty of video story.

Natural Language Processing Question Answering +2

Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

no code implementations17 Jan 2020 Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang

Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video.

Graph Learning Video Understanding

Compositional Structure Learning for Sequential Video Data

no code implementations3 Jul 2019 Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang

However, most of sequential data, as seen with videos, have complex temporal dependencies that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods.

Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies

no code implementations20 Jan 2019 Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang

While conventional methods for sequential learning focus on interaction between consecutive inputs, we suggest a new method which captures composite semantic flows with variable-length dependencies.

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