activity
20172020
most citedA Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video

7 citations · 19 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CV2020

Parameter Efficient Multimodal Transformers for Video Representation Learning

Sangho Lee, Youngjae Yu, Gunhee Kim +3

The recent success of Transformers in the language domain has motivated adapting it to a multimodal setting, where a new visual model is trained in tandem with an already pretraine…

cs.CL20203 cited

Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context

Hankyol Lee, Youngjae Yu, Gunhee Kim

We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also m…

cs.CV2020

CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data

Youngjae Yu, Seunghwan Lee, Yuncheol Choi +1

We present an approach named CurlingNet that can measure the semantic distance of composition of image-text embedding. In order to learn an effective image-text composition for the…

cs.CV2018

A Joint Sequence Fusion Model for Video Question Answering and Retrieval

Youngjae Yu, Jongseok Kim, Gunhee Kim

We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e.g. a video clip and a language s…

cs.CV2018

A Memory Network Approach for Story-based Temporal Summarization of 360° Videos

Sangho Lee, Jinyoung Sung, Youngjae Yu +1

We address the problem of story-based temporal summarization of long 360° videos. We propose a novel memory network model named Past-Future Memory Network (PFMN), in which we first…

cs.CV20187 cited

A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video

Youngjae Yu, Sangho Lee, Joonil Na +2

We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly…