1 code implementation • 21 Mar 2024 • Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
Through a series of observations, we find that the prompt choice significantly affects the calibration in CLIP, where the prompts leading to higher text feature dispersion result in better-calibrated predictions.
no code implementations • 18 Mar 2024 • SooHwan Eom, Eunseop Yoon, Hee Suk Yoon, Chanwoo Kim, Mark Hasegawa-Johnson, Chang D. Yoo
In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the generation of narrowly focused output distributions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 15 Dec 2023 • Sunjae Yoon, Dahyun Kim, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chnag D. Yoo
Video-grounded Dialogue (VGD) aims to answer questions regarding a given multi-modal input comprising video, audio, and dialogue history.
1 code implementation • 10 Dec 2023 • Hyun Ryu, Sunjae Yoon, Hee Suk Yoon, Eunseop Yoon, Chang D. Yoo
Our experimental results support that SimPSI considerably enhances the performance of time series data augmentations by preserving core spectral information.
no code implementations • 16 Aug 2023 • Eunseop Yoon, Hee Suk Yoon, Dhananjaya Gowda, SooHwan Eom, Daehyeok Kim, John Harvill, Heting Gao, Mark Hasegawa-Johnson, Chanwoo Kim, Chang D. Yoo
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction.
no code implementations • 25 May 2023 • Eunseop Yoon, Hee Suk Yoon, John Harvill, Mark Hasegawa-Johnson, Chang D. Yoo
INTapt is trained simultaneously in the following two manners: (1) adversarial training to reduce accent feature dependence between the original input and the prompt-concatenated input and (2) training to minimize CTC loss for improving ASR performance to a prompt-concatenated input.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 4 Mar 2023 • Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions.
no code implementations • 14 Dec 2022 • Hee Suk Yoon, Eunseop Yoon, John Harvill, Sunjae Yoon, Mark Hasegawa-Johnson, Chang D. Yoo
To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word.
no code implementations • 12 Dec 2022 • Sunjae Yoon, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, Chang D. Yoo
Despite the recent success of multi-modal reasoning to generate answer sentences, existing dialogue systems still suffer from a text hallucination problem, which denotes indiscriminate text-copying from input texts without an understanding of the question.
1 code implementation • 17 Oct 2022 • Sunjae Yoon, Ji Woo Hong, Eunseop Yoon, Dahyun Kim, Junyeong Kim, Hee Suk Yoon, Chang D. Yoo
Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query.