Search Results for author: Donghyun Kim

Found 67 papers, 32 papers with code

CREPE: Coordinate-Aware End-to-End Document Parser

no code implementations1 May 2024 Yamato Okamoto, Youngmin Baek, Geewook Kim, Ryota Nakao, Donghyun Kim, Moon Bin Yim, Seunghyun Park, Bado Lee

CREPE's abilities including OCR and semantic parsing not only mitigate error propagation issues in existing OCR-dependent methods, it also significantly enhance the functionality of sequence generation models, ushering in a new era for document understanding studies.

document understanding Optical Character Recognition (OCR) +3

Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot

no code implementations23 Apr 2024 Neil Guan, Shangqun Yu, Shifan Zhu, Donghyun Kim

Replicating the remarkable athleticism seen in animals has long been a challenge in robotics control.

Reinforcement Learning (RL)

CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models

1 code implementation18 Apr 2024 Minjung Shin, Donghyun Kim, Jeh-Kwang Ryu

We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties.

Question Generation Question-Generation

Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System

1 code implementation17 Apr 2024 Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, MinChul Yang, Chanyoung Park

Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge.

Collaborative Filtering Recommendation Systems

HyperCLOVA X Technical Report

no code implementations2 Apr 2024 Kang Min Yoo, Jaegeun Han, Sookyo In, Heewon Jeon, Jisu Jeong, Jaewook Kang, Hyunwook Kim, Kyung-Min Kim, Munhyong Kim, Sungju Kim, Donghyun Kwak, Hanock Kwak, Se Jung Kwon, Bado Lee, Dongsoo Lee, Gichang Lee, Jooho Lee, Baeseong Park, Seongjin Shin, Joonsang Yu, Seolki Baek, Sumin Byeon, Eungsup Cho, Dooseok Choe, Jeesung Han, Youngkyun Jin, Hyein Jun, Jaeseung Jung, Chanwoong Kim, jinhong Kim, Jinuk Kim, Dokyeong Lee, Dongwook Park, Jeong Min Sohn, Sujung Han, Jiae Heo, Sungju Hong, Mina Jeon, Hyunhoon Jung, Jungeun Jung, Wangkyo Jung, Chungjoon Kim, Hyeri Kim, Jonghyun Kim, Min Young Kim, Soeun Lee, Joonhee Park, Jieun Shin, Sojin Yang, Jungsoon Yoon, Hwaran Lee, Sanghwan Bae, Jeehwan Cha, Karl Gylleus, Donghoon Ham, Mihak Hong, Youngki Hong, Yunki Hong, Dahyun Jang, Hyojun Jeon, Yujin Jeon, Yeji Jeong, Myunggeun Ji, Yeguk Jin, Chansong Jo, Shinyoung Joo, Seunghwan Jung, Adrian Jungmyung Kim, Byoung Hoon Kim, Hyomin Kim, Jungwhan Kim, Minkyoung Kim, Minseung Kim, Sungdong Kim, Yonghee Kim, Youngjun Kim, Youngkwan Kim, Donghyeon Ko, Dughyun Lee, Ha Young Lee, Jaehong Lee, Jieun Lee, Jonghyun Lee, Jongjin Lee, Min Young Lee, Yehbin Lee, Taehong Min, Yuri Min, Kiyoon Moon, Hyangnam Oh, Jaesun Park, Kyuyon Park, Younghun Park, Hanbae Seo, Seunghyun Seo, Mihyun Sim, Gyubin Son, Matt Yeo, Kyung Hoon Yeom, Wonjoon Yoo, Myungin You, Doheon Ahn, Homin Ahn, Joohee Ahn, Seongmin Ahn, Chanwoo An, Hyeryun An, Junho An, Sang-Min An, Boram Byun, Eunbin Byun, Jongho Cha, Minji Chang, Seunggyu Chang, Haesong Cho, Youngdo Cho, Dalnim Choi, Daseul Choi, Hyoseok Choi, Minseong Choi, Sangho Choi, Seongjae Choi, Wooyong Choi, Sewhan Chun, Dong Young Go, Chiheon Ham, Danbi Han, Jaemin Han, Moonyoung Hong, Sung Bum Hong, Dong-Hyun Hwang, Seongchan Hwang, Jinbae Im, Hyuk Jin Jang, Jaehyung Jang, Jaeni Jang, Sihyeon Jang, Sungwon Jang, Joonha Jeon, Daun Jeong, JoonHyun Jeong, Kyeongseok Jeong, Mini Jeong, Sol Jin, Hanbyeol Jo, Hanju Jo, Minjung Jo, Chaeyoon Jung, Hyungsik Jung, Jaeuk Jung, Ju Hwan Jung, Kwangsun Jung, Seungjae Jung, Soonwon Ka, Donghan Kang, Soyoung Kang, Taeho Kil, Areum Kim, Beomyoung Kim, Byeongwook Kim, Daehee Kim, Dong-Gyun Kim, Donggook Kim, Donghyun Kim, Euna Kim, Eunchul Kim, Geewook Kim, Gyu Ri Kim, Hanbyul Kim, Heesu Kim, Isaac Kim, Jeonghoon Kim, JiHye Kim, Joonghoon Kim, Minjae Kim, Minsub Kim, Pil Hwan Kim, Sammy Kim, Seokhun Kim, Seonghyeon Kim, Soojin Kim, Soong Kim, Soyoon Kim, Sunyoung Kim, TaeHo Kim, Wonho Kim, Yoonsik Kim, You Jin Kim, Yuri Kim, Beomseok Kwon, Ohsung Kwon, Yoo-Hwan Kwon, Anna Lee, Byungwook Lee, Changho Lee, Daun Lee, Dongjae Lee, Ha-Ram Lee, Hodong Lee, Hwiyeong Lee, Hyunmi Lee, Injae Lee, Jaeung Lee, Jeongsang Lee, Jisoo Lee, JongSoo Lee, Joongjae Lee, Juhan Lee, Jung Hyun Lee, Junghoon Lee, Junwoo Lee, Se Yun Lee, Sujin Lee, Sungjae Lee, Sungwoo Lee, Wonjae Lee, Zoo Hyun Lee, Jong Kun Lim, Kun Lim, Taemin Lim, Nuri Na, Jeongyeon Nam, Kyeong-Min Nam, Yeonseog Noh, Biro Oh, Jung-Sik Oh, Solgil Oh, Yeontaek Oh, Boyoun Park, Cheonbok Park, Dongju Park, Hyeonjin Park, Hyun Tae Park, Hyunjung Park, JiHye Park, Jooseok Park, JungHwan Park, Jungsoo Park, Miru Park, Sang Hee Park, Seunghyun Park, Soyoung Park, Taerim Park, Wonkyeong Park, Hyunjoon Ryu, Jeonghun Ryu, Nahyeon Ryu, Soonshin Seo, Suk Min Seo, Yoonjeong Shim, Kyuyong Shin, Wonkwang Shin, Hyun Sim, Woongseob Sim, Hyejin Soh, Bokyong Son, Hyunjun Son, Seulah Son, Chi-Yun Song, Chiyoung Song, Ka Yeon Song, Minchul Song, Seungmin Song, Jisung Wang, Yonggoo Yeo, Myeong Yeon Yi, Moon Bin Yim, Taehwan Yoo, Youngjoon Yoo, Sungmin Yoon, Young Jin Yoon, Hangyeol Yu, Ui Seon Yu, Xingdong Zuo, Jeongin Bae, Joungeun Bae, Hyunsoo Cho, Seonghyun Cho, Yongjin Cho, Taekyoon Choi, Yera Choi, Jiwan Chung, Zhenghui Han, Byeongho Heo, Euisuk Hong, Taebaek Hwang, Seonyeol Im, Sumin Jegal, Sumin Jeon, Yelim Jeong, Yonghyun Jeong, Can Jiang, Juyong Jiang, Jiho Jin, Ara Jo, Younghyun Jo, Hoyoun Jung, Juyoung Jung, Seunghyeong Kang, Dae Hee Kim, Ginam Kim, Hangyeol Kim, Heeseung Kim, Hyojin Kim, Hyojun Kim, Hyun-Ah Kim, Jeehye Kim, Jin-Hwa Kim, Jiseon Kim, Jonghak Kim, Jung Yoon Kim, Rak Yeong Kim, Seongjin Kim, Seoyoon Kim, Sewon Kim, Sooyoung Kim, Sukyoung Kim, Taeyong Kim, Naeun Ko, Bonseung Koo, Heeyoung Kwak, Haena Kwon, Youngjin Kwon, Boram Lee, Bruce W. Lee, Dagyeong Lee, Erin Lee, Euijin Lee, Ha Gyeong Lee, Hyojin Lee, Hyunjeong Lee, Jeeyoon Lee, Jeonghyun Lee, Jongheok Lee, Joonhyung Lee, Junhyuk Lee, Mingu Lee, Nayeon Lee, Sangkyu Lee, Se Young Lee, Seulgi Lee, Seung Jin Lee, Suhyeon Lee, Yeonjae Lee, Yesol Lee, Youngbeom Lee, Yujin Lee, Shaodong Li, Tianyu Liu, Seong-Eun Moon, Taehong Moon, Max-Lasse Nihlenramstroem, Wonseok Oh, Yuri Oh, Hongbeen Park, Hyekyung Park, Jaeho Park, Nohil Park, Sangjin Park, Jiwon Ryu, Miru Ryu, Simo Ryu, Ahreum Seo, Hee Seo, Kangdeok Seo, Jamin Shin, Seungyoun Shin, Heetae Sin, Jiangping Wang, Lei Wang, Ning Xiang, Longxiang Xiao, Jing Xu, Seonyeong Yi, Haanju Yoo, Haneul Yoo, Hwanhee Yoo, Liang Yu, Youngjae Yu, Weijie Yuan, Bo Zeng, Qian Zhou, Kyunghyun Cho, Jung-Woo Ha, Joonsuk Park, Jihyun Hwang, Hyoung Jo Kwon, Soonyong Kwon, Jungyeon Lee, Seungho Lee, Seonghyeon Lim, Hyunkyung Noh, Seungho Choi, Sang-Woo Lee, Jung Hwa Lim, Nako Sung

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.

Instruction Following Machine Translation +1

Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation

1 code implementation1 Apr 2024 Beomyoung Kim, Donghyun Kim, Sung Ju Hwang

This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our empirical findings.

object-detection Object Detection +3

DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs

1 code implementation28 Mar 2024 Donghyun Kim, Byeongho Heo, Dongyoon Han

This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures.

Instance Segmentation object-detection +2

WoLF: Wide-scope Large Language Model Framework for CXR Understanding

no code implementations19 Mar 2024 Seil Kang, Donghyun Kim, Junhyeok Kim, Hyo Kyung Lee, Seong Jae Hwang

(1) Previous methods solely use CXR reports, which are insufficient for comprehensive Visual Question Answering (VQA), especially when additional health-related data like medication history and prior diagnoses are needed.

Anatomy Instruction Following +4

A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting

no code implementations29 Feb 2024 Young-Jin Park, Donghyun Kim, Frédéric Odermatt, Juho Lee, Kyung-Min Kim

Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization.

Time Series Time Series Forecasting +1

Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing

no code implementations9 Feb 2024 Hochul Hwang, Sunjae Kwon, Yekyung Kim, Donghyun Kim

Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues.

Decision Making

Adaptive Self-training Framework for Fine-grained Scene Graph Generation

1 code implementation18 Jan 2024 Kibum Kim, Kanghoon Yoon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park

To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained.

Graph Generation Scene Graph Generation

What, How, and When Should Object Detectors Update in Continually Changing Test Domains?

no code implementations12 Dec 2023 Jayeon Yoo, Dongkwan Lee, Inseop Chung, Donghyun Kim, Nojun Kwak

It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time.

object-detection Object Detection +1

Learning Human Action Recognition Representations Without Real Humans

1 code implementation NeurIPS 2023 Howard Zhong, Samarth Mishra, Donghyun Kim, SouYoung Jin, Rameswar Panda, Hilde Kuehne, Leonid Karlinsky, Venkatesh Saligrama, Aude Oliva, Rogerio Feris

To this end, we present, for the first time, a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model.

Action Recognition Ethics +2

LLM4SGG: Large Language Models for Weakly Supervised Scene Graph Generation

1 code implementation16 Oct 2023 Kibum Kim, Kanghoon Yoon, Jaehyeong Jeon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park

Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations.

Few-Shot Learning Large Language Model +2

SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain Gap

no code implementations ICCV 2023 Daehee Kim, Yoonsik Kim, Donghyun Kim, Yumin Lim, Geewook Kim, Taeho Kil

In this paper, we investigate effective pre-training tasks in the broader domains and also propose a novel pre-training method called SCOB that leverages character-wise supervised contrastive learning with online text rendering to effectively pre-train document and scene text domains by bridging the domain gap.

Contrastive Learning document understanding +2

Universal Metric Learning with Parameter-Efficient Transfer Learning

no code implementations16 Sep 2023 Sungyeon Kim, Donghyun Kim, Suha Kwak

In this regard, we introduce a novel metric learning paradigm, called Universal Metric Learning (UML), which learns a unified distance metric capable of capturing relations across multiple data distributions.

Metric Learning Transfer Learning

Task Relation-aware Continual User Representation Learning

1 code implementation1 Jun 2023 Sein Kim, Namkyeong Lee, Donghyun Kim, MinChul Yang, Chanyoung Park

However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks.

Continual Learning Relation +1

Learning low-dimensional dynamics from whole-brain data improves task capture

no code implementations18 May 2023 Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit Kashyap, Sergey Plis, Vince Calhoun

We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.

Dimensionality Reduction

Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface

no code implementations15 May 2023 Shifan Zhu, Zhipeng Tang, Michael Yang, Erik Learned-Miller, Donghyun Kim

Our paper proposes a direct sparse visual odometry method that combines event and RGB-D data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors.

Pose Estimation Visual Odometry

Going Beyond Nouns With Vision & Language Models Using Synthetic Data

1 code implementation ICCV 2023 Paola Cascante-Bonilla, Khaled Shehada, James Seale Smith, Sivan Doveh, Donghyun Kim, Rameswar Panda, Gül Varol, Aude Oliva, Vicente Ordonez, Rogerio Feris, Leonid Karlinsky

We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models.

Sentence Visual Reasoning

Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection

1 code implementation26 Mar 2023 Kuniaki Saito, Donghyun Kim, Piotr Teterwak, Rogerio Feris, Kate Saenko

We propose to use Relative Gradient Norm (RGN) as a way to measure the vulnerability of a backbone to feature distortion, and show that high RGN is indeed correlated with lower OOD performance.

object-detection Robust Object Detection

Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation

1 code implementation27 Nov 2022 Kaihong Wang, Donghyun Kim, Rogerio Feris, Kate Saenko, Margrit Betke

We propose to perform adaptation on attention maps with cross-domain attention layers that share features between the source and the target domains.

Semantic Segmentation Unsupervised Domain Adaptation

ConStruct-VL: Data-Free Continual Structured VL Concepts Learning

1 code implementation CVPR 2023 James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, Leonid Karlinsky

This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting.

On Web-based Visual Corpus Construction for Visual Document Understanding

1 code implementation7 Nov 2022 Donghyun Kim, Teakgyu Hong, Moonbin Yim, Yoonsik Kim, Geewook Kim

In recent years, research on visual document understanding (VDU) has grown significantly, with a particular emphasis on the development of self-supervised learning methods.

document understanding Optical Character Recognition (OCR) +1

Grafting Vision Transformers

no code implementations28 Oct 2022 Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim, Shivchander Sudalairaj, Quanfu Fan, Michael S. Ryoo

In this paper, we present a simple and efficient add-on component (termed GrafT) that considers global dependencies and multi-scale information throughout the network, in both high- and low-resolution features alike.

Image Classification Instance Segmentation +3

System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work

no code implementations24 Oct 2022 Hochul Hwang, Tim Xia, Ibrahima Keita, Ken Suzuki, Joydeep Biswas, Sunghoon I. Lee, Donghyun Kim

A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, potential for mass production, and low maintenance burden.

Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances

no code implementations NAACL 2022 Wongyu Kim, Youbin Ahn, Donghyun Kim, Kyong-Ho Lee

To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention.

Empathetic Response Generation Response Generation

Temporal Relevance Analysis for Video Action Models

no code implementations25 Apr 2022 Quanfu Fan, Donghyun Kim, Chun-Fu, Chen, Stan Sclaroff, Kate Saenko, Sarah Adel Bargal

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.

Action Recognition

A Unified Framework for Domain Adaptive Pose Estimation

1 code implementation1 Apr 2022 Donghyun Kim, Kaihong Wang, Kate Saenko, Margrit Betke, Stan Sclaroff

In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision.

2D Pose Estimation Animal Pose Estimation +2

A Broad Study of Pre-training for Domain Generalization and Adaptation

1 code implementation22 Mar 2022 Donghyun Kim, Kaihong Wang, Stan Sclaroff, Kate Saenko

In this paper, we provide a broad study and in-depth analysis of pre-training for domain adaptation and generalization, namely: network architectures, size, pre-training loss, and datasets.

Domain Generalization

Robust Convergence in Federated Learning through Label-wise Clustering

no code implementations28 Dec 2021 Hunmin Lee, Yueyang Liu, Donghyun Kim, Yingshu Li

Non-IID dataset and heterogeneous environment of the local clients are regarded as a major issue in Federated Learning (FL), causing a downturn in the convergence without achieving satisfactory performance.

Clustering Federated Learning

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers

1 code implementation28 May 2021 Kuniaki Saito, Donghyun Kim, Kate Saenko

OpenMatch achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.

Novelty Detection Outlier Detection

Predicting Participation in Cancer Screening Programs with Machine Learning

no code implementations27 Jan 2021 Donghyun Kim

In this paper, we present machine learning models based on random forest classifiers, support vector machines, gradient boosted decision trees, and artificial neural networks to predict participation in cancer screening programs in South Korea.

BIG-bench Machine Learning

CDS: Cross-Domain Self-Supervised Pre-Training

no code implementations ICCV 2021 Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A. Plummer, Stan Sclaroff, Kate Saenko

We present a two-stage pre-training approach that improves the generalization ability of standard single-domain pre-training.

Domain Adaptation Transfer Learning

BROS: A Pre-trained Language Model for Understanding Texts in Document

no code implementations1 Jan 2021 Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park

Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts.

Decoder Document Layout Analysis +3

Self-supervised Visual Attribute Learning for Fashion Compatibility

no code implementations1 Aug 2020 Donghyun Kim, Kuniaki Saito, Samarth Mishra, Stan Sclaroff, Kate Saenko, Bryan A Plummer

Our approach consists of three self-supervised tasks designed to capture different concepts that are neglected in prior work that we can select from depending on the needs of our downstream tasks.

Attribute Object Recognition +3

Unsupervised Differentiable Multi-aspect Network Embedding

1 code implementation7 Jun 2020 Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, Jiawei Han

To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.

Clustering Graph Clustering +2

Learning to Scale Multilingual Representations for Vision-Language Tasks

no code implementations ECCV 2020 Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer

Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added.

Language Modelling Machine Translation +4

Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels

no code implementations18 Mar 2020 Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A. Plummer, Stan Sclaroff, Kate Saenko

We show that when labeled source examples are limited, existing methods often fail to learn discriminative features applicable for both source and target domains.

Self-Supervised Learning Unsupervised Domain Adaptation

Universal Domain Adaptation through Self Supervision

1 code implementation NeurIPS 2020 Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko

While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.

Clustering Partial Domain Adaptation +2

MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention

no code implementations18 Feb 2020 Donghyun Kim, Tian Lan, Chuhang Zou, Ning Xu, Bryan A. Plummer, Stan Sclaroff, Jayan Eledath, Gerard Medioni

We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.

Multi-Task Learning

Unsupervised Attributed Multiplex Network Embedding

2 code implementations15 Nov 2019 Chanyoung Park, Donghyun Kim, Jiawei Han, Hwanjo Yu

Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.

Network Embedding Relation

MULE: Multimodal Universal Language Embedding

no code implementations8 Sep 2019 Donghyun Kim, Kuniaki Saito, Kate Saenko, Stan Sclaroff, Bryan A. Plummer

In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages.

Data Augmentation Machine Translation +3

Collaborative Translational Metric Learning

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Xing Xie, Hwanjo Yu

We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.

Knowledge Graph Embedding Metric Learning +1

Task-Guided Pair Embedding in Heterogeneous Network

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu

In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).

Network Embedding

Semi-supervised Domain Adaptation via Minimax Entropy

3 code implementations ICCV 2019 Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, Kate Saenko

Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision.

Domain Adaptation Semi-supervised Domain Adaptation

Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues

no code implementations NAACL 2019 Sungjoon Park, Donghyun Kim, Alice Oh

A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome.

Language Modelling

Learning to Select: Problem, Solution, and Applications

no code implementations ICLR 2018 Heechang Ryu, Donghyun Kim, Hayong Shin

For example, job dispatching in the manufacturing factory is a typical "Learning to Select" problem.

Learning-To-Rank

Excitation Backprop for RNNs

1 code implementation CVPR 2018 Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, Stan Sclaroff

Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.

Action Recognition Temporal Action Localization +1

Click-aware purchase prediction with push at the top

no code implementations21 Jun 2017 Chanyoung Park, Donghyun Kim, Min-Chul Yang, Jung-Tae Lee, Hwanjo Yu

We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records.

Learning-To-Rank

Deep 3D Face Identification

no code implementations30 Mar 2017 Donghyun Kim, Matthias Hernandez, Jongmoo Choi, Gerard Medioni

We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan.

Face Identification Face Recognition +1

Convolutional Matrix Factorization for Document Context-Aware Recommendation

1 code implementation RecSys 2016 Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Y

However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents.

Recommendation Systems

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