7.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- NAVER Cloud (South Korea)KR46 papers
- Seoul National UniversityKR27 papers
- Korea Advanced Institute of Science and TechnologyKR24 papers
- Yonsei UniversityKR17 papers
- Sungkyunkwan UniversityKR12 papers
- Korea UniversityKR9 papers
- Line Corporation (Japan)JP5 papers
- Virginia TechUS5 papers
- Inha UniversityKR4 papers
- Kootenay Association for Science & TechnologyCA4 papers
- University of TorontoCA4 papers
- Hong Kong University of Science and TechnologyHK3 papers
23 papers · 1 filter
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning
Euna Jung, Jaekeol Choi, Wonjong Rhee
A BERT-based Neural Ranking Model (NRM) can be either a crossencoder or a bi-encoder. Between the two, bi-encoder is highly efficient because all the documents can be pre-processed…
Global-Local Item Embedding for Temporal Set Prediction
Seungjae Jung, Young-Jin Park, Jisu Jeong +4
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e.g., personalized purchase prediction of shoppi…
Look Who's Talking: Active Speaker Detection in the Wild
You Jin Kim, Hee-Soo Heo, Soyeon Choe +5
In this work, we present a novel audio-visual dataset for active speaker detection in the wild. A speaker is considered active when his or her face is visible and the voice is audi…
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu +2
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and ta…
Weakly Supervised Pre-Training for Multi-Hop Retriever
Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim +2
In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question de…
Self-Guided Contrastive Learning for BERT Sentence Representations
Taeuk Kim, Kang Min Yoo, Sang-goo Lee
Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we…
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Sungmin Cha, Beomyoung Kim, Youngjoon Yoo +1
This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowl…
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings
Marcely Zanon Boito, Bolaji Yusuf, Lucas Ondel +2
Documenting languages helps to prevent the extinction of endangered dialects, many of which are otherwise expected to disappear by the end of the century. When documenting oral lan…
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
Sungdong Kim, Minsuk Chang, Sang-Woo Lee
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generate…
SBNet: Segmentation-based Network for Natural Language-based Vehicle Search
Sangrok Lee, Taekang Woo, Sang Hun Lee
Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied…
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization
Daehee Kim, Seunghyun Park, Jinkyu Kim +1
In general, an experimental environment for deep learning assumes that the training and the test dataset are sampled from the same distribution. However, in real-world situations,…
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network
Jee-weon Jung, Hee-Soo Heo, Youngki Kwon +2
In this work, we propose an overlapped speech detection system trained as a three-class classifier. Unlike conventional systems that perform binary classification as to whether or…
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Song Park, Sanghyuk Chun, Junbum Cha +2
A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the…
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
Solene Evain, Ha Nguyen, Hang Le +15
Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. T…
Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification
Sangrok Lee, Taekang Woo, Sang Hun Lee
Vehicle re-identification helps in distinguishing between images of the same and other vehicles. It is a challenging process because of significant intra-instance differences betwe…
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang +2
Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct c…
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models
Lorenzo Lupo, Marco Dinarelli, Laurent Besacier
Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual informatio…
Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation
Jaekeol Choi, Euna Jung, Jangwon Suh +1
BERT-based Neural Ranking Models (NRMs) can be classified according to how the query and document are encoded through BERT's self-attention layers - bi-encoder versus cross-encoder…
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics
Vassilina Nikoulina, Maxat Tezekbayev, Nuradil Kozhakhmet +3
There is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether lin…
M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Kyung-Wha Park, Jung-Woo Ha, JungHoon Lee +3
Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neu…
SWAD: Domain Generalization by Seeking Flat Minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee +4
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains. Although a variety of DG methods…
Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss
Eunwoo Song, Ryuichi Yamamoto, Min-Jae Hwang +3
This paper proposes a spectral-domain perceptual weighting technique for Parallel WaveGAN-based text-to-speech (TTS) systems. The recently proposed Parallel WaveGAN vocoder success…
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time
Kibeom Hong, Youngjung Uh, Hyeran Byun
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture consid…