94 citations · 454 across the 21 of their papers we have counts for
7 papers · 1 filter
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
Byeongchang Kim, Hyunwoo Kim, Gunhee Kim
We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts fr…
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…
Video Prediction with Appearance and Motion Conditions
Yunseok Jang, Gunhee Kim, Yale Song
Video prediction aims to generate realistic future frames by learning dynamic visual patterns. One fundamental challenge is to deal with future uncertainty: How should a model beha…
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…
A Hierarchical Latent Structure for Variational Conversation Modeling
Yookoon Park, Jaemin Cho, Gunhee Kim
Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration…
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks
Youngjin Kim, Minjung Kim, Gunhee Kim
We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple…
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…