7.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Naver (South Korea)KR46 papers
- Korea Advanced Institute of Science and TechnologyKR10 papers
- Seoul National UniversityKR7 papers
- Sungkyunkwan UniversityKR6 papers
- University of TorontoCA4 papers
- Virginia TechUS4 papers
- Yonsei UniversityKR4 papers
- Korea UniversityKR3 papers
- Georgia Institute of TechnologyUS2 papers
- Kootenay Association for Science & TechnologyCA2 papers
- Pusan National UniversityKR2 papers
- Wonkwang UniversityKR2 papers
12 papers · 1 filter
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding
Sunoh Kim, Jungchan Cho, Joonsang Yu +2
In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by…
Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication
Hyunmin Choi, Simon Woo, Hyoungshick Kim
Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns…
MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling
Taewan Kim, Seolyeong Bae, Hyun Ah Kim +4
In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about th…
HPCClusterScape: Increasing Transparency and Efficiency of Shared High-Performance Computing Clusters for Large-scale AI Models
Heungseok Park, Aeree Cho, Hyojun Jeon +5
The emergence of large-scale AI models, like GPT-4, has significantly impacted academia and industry, driving the demand for high-performance computing (HPC) to accelerate workload…
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria
Tae Soo Kim, Yoonjoo Lee, Jamin Shin +2
By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must…
ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events
Woosuk Seo, Chanmo Yang, Young-Ho Kim
Children typically learn to identify and express emotions through sharing their stories and feelings with others, particularly their family. However, it is challenging for parents…
Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech
Hyungchan Yoon, Changhwan Kim, Eunwoo Song +2
For personalized speech generation, a neural text-to-speech (TTS) model must be successfully implemented with limited data from a target speaker. To this end, the baseline TTS mode…
Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation
Hanbyul Kim, Seunghyun Seo, Lukas Lee +1
Punctuated text prediction is crucial for automatic speech recognition as it enhances readability and impacts downstream natural language processing tasks. In streaming scenarios,…
An empirical study on speech restoration guided by self supervised speech representation
Jaeuk Byun, Youna Ji, Soo Whan Chung +2
Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additive noise, reverberation, clipping…
k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment
Minkyu Jeon, Hyeonjin Park, Hyunwoo J. Kim +2
The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally…
AVscript: Accessible Video Editing with Audio-Visual Scripts
Mina Huh, Saelyne Yang, Yi-Hao Peng +3
Sighted and blind and low vision (BLV) creators alike use videos to communicate with broad audiences. Yet, video editing remains inaccessible to BLV creators. Our formative study r…
Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data
Jing Wei, Sungdong Kim, Hyunhoon Jung +1
Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on natur…