1 code implementation • DMR (COLING) 2020 • Hyonsu Choe, Jiyoon Han, Hyejin Park, Tae Hwan Oh, Hansaem Kim
To explore the potential sembanking in Korean and ways to represent the meaning of Korean sentences, this paper reports on the process of applying Abstract Meaning Representation to Korean, a semantic representation framework that has been studied in wide range of languages, and its output: the Korean AMR corpus.
no code implementations • 12 Apr 2024 • Hyesong Choi, Hunsang Lee, Seyoung Joung, Hyejin Park, Jiyeong Kim, Dongbo Min
Initially, we delve into an exploration of the inherent properties that a masked token ought to possess.
no code implementations • 12 Apr 2024 • Hyesong Choi, Hyejin Park, Kwang Moo Yi, Sungmin Cha, Dongbo Min
In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-effective approach that significantly enhances the pre-training performance of Masked Image Modeling (MIM) approaches by prioritizing token salience.
no code implementations • 28 Mar 2024 • Hyejin Park, Jeongyeon Hwang, Sunung Mun, Sangdon Park, Jungseul Ok
In response to the emerging threat, we propose median batch normalization (MedBN), leveraging the robustness of the median for statistics estimation within the batch normalization layer during test-time inference.
no code implementations • 3 Feb 2024 • Mahathir Monjur, Jia Liu, Jingye Xu, Yuntong Zhang, Xiaomeng Wang, Chengdong Li, Hyejin Park, Wei Wang, Karl Shieh, Sirajum Munir, Jing Wang, Lixin Song, Shahriar Nirjon
This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios.
1 code implementation • 8 Jul 2022 • Clive Gomes, Hyejin Park, Patrick Kollman, Yi Song, Iffanice Houndayi, Ankit Shah
This project involved participation in the DCASE 2022 Competition (Task 6) which had two subtasks: (1) Automated Audio Captioning and (2) Language-Based Audio Retrieval.
no code implementations • 4 Mar 2021 • Hyejin Park, Taaha Waseem, Wen Qi Teo, Ying Hwei Low, Mei Kuan Lim, Chun Yong Chong
Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision.
no code implementations • 4 Feb 2021 • Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok
To cope with the latent structural parameter, we consider a transfer learning setting in which an agent must learn to transfer the structural information from the prior tasks to the next task, which is inspired by practical problems such as rate adaptation in wireless link.
no code implementations • WS 2019 • Hyonsu Choe, Jiyoon Han, Hyejin Park, Hansaem Kim
This paper concerns the application of Abstract Meaning Representation (AMR) to Korean.