5 citations · 15 across the 7 of their papers we have counts for
8 papers
Video-Text Representation Learning via Differentiable Weak Temporal Alignment
Dohwan Ko, Joonmyung Choi, Juyeon Ko +4
Learning generic joint representations for video and text by a supervised method requires a prohibitively substantial amount of manually annotated video datasets. As a practical al…
MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection
Bumsoo Kim, Jonghwan Mun, Kyoung-Woon On +3
Human-Object Interaction (HOI) detection is the task of identifying a set of <human, object, interaction> triplets from an image. Recent work proposed transformer encoder-decoder a…
Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts
Minchul Shin, Jonghwan Mun, Kyoung-Woon On +3
The VALUE (Video-And-Language Understanding Evaluation) benchmark is newly introduced to evaluate and analyze multi-modal representation learning algorithms on three video-and-lang…
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
Seongho Choi, Kyoung-Woon On, Yu-Jung Heo +4
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 diffi…
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data
Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo +1
Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However,…
Compositional Structure Learning for Sequential Video Data
Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo +1
Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However,…