94 citations · 454 across the 21 of their papers we have counts for
21 papers
Variational Laplace Autoencoders
Yookoon Park, Chris Dongjoo Kim, Gunhee Kim
Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. However, such amortized variational inference faces two challenges: (…
Panoramic Vision Transformer for Saliency Detection in 360° Videos
Heeseung Yun, Sehun Lee, Gunhee Kim
360 video saliency detection is one of the challenging benchmarks for 360 video understanding since non-negligible distortion and discontinuity occur in the project…
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…
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…
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Soochan Lee, Junsoo Ha, Dongsu Zhang +1
Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and tas…
Discovery of Natural Language Concepts in Individual Units of CNNs
Seil Na, Yo Joong Choe, Dong-Hyun Lee +1
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpre…
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
Soochan Lee, Junsoo Ha, Gunhee Kim
Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, whic…
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…
A Read-Write Memory Network for Movie Story Understanding
Seil Na, Sangho Lee, Jisung Kim +1
We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding. The ke…
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
Youngjae Yu, Jongwook Choi, Yeonhwa Kim +3
The attention mechanisms in deep neural networks are inspired by human's attention that sequentially focuses on the most relevant parts of the information over time to generate pre…
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
Seil Na, Youngjae Yu, Sangho Lee +2
YouTube-8M is the largest video dataset for multi-label video classification. In order to tackle the multi-label classification on this challenging dataset, it is necessary to solv…
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
Cesc Chunseong Park, Byeongchang Kim, Gunhee Kim
We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, account…
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
Yunseok Jang, Yale Song, Youngjae Yu +2
Vision and language understanding has emerged as a subject undergoing intense study in Artificial Intelligence. Among many tasks in this line of research, visual question answering…
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
Youngjae Yu, Hyungjin Ko, Jongwook Choi +1
We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful se…
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines
Hao Zhang, Zhiting Hu, Jinliang Wei +4
Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep…