activity
20212023
most citedSCANet: Scene Complexity Aware Network for Weakly-Supervised Video Moment Retrieval

26 citations · 51 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023★ 2 cited

HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue

Sunjae Yoon, Dahyun Kim, Eunseop Yoon +3

Video-grounded Dialogue (VGD) aims to answer questions regarding a given multi-modal input comprising video, audio, and dialogue history. Although there have been numerous efforts…

cs.CV2023★ 26 cited

SCANet: Scene Complexity Aware Network for Weakly-Supervised Video Moment Retrieval

Sunjae Yoon, Gwanhyeong Koo, Dahyun Kim +1

Video moment retrieval aims to localize moments in video corresponding to a given language query. To avoid the expensive cost of annotating the temporal moments, weakly-supervised…

cs.CV2023

Recursive Video Lane Detection

Dongkwon Jin, Dahyun Kim, Chang-Su Kim

A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively…

cs.CV2023★ 1 cited

Progressive Fourier Neural Representation for Sequential Video Compilation

Haeyong Kang, Jaehong Yoon, DaHyun Kim +2

Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space a…

cs.CV2022★ 22 cited

Selective Query-guided Debiasing for Video Corpus Moment Retrieval

Sunjae Yoon, Ji Woo Hong, Eunseop Yoon +4

Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query. Existing retrieval systems tend to rely on retrieval bias as a…

cs.CV2021

Structured Co-reference Graph Attention for Video-grounded Dialogue

Junyeong Kim, Sunjae Yoon, Dahyun Kim +1

A video-grounded dialogue system referred to as the Structured Co-reference Graph Attention (SCGA) is presented for decoding the answer sequence to a question regarding a given vid…