no code implementations • 8 Feb 2024 • Youngsik Yun, Jihie Kim
Image Captioning generates descriptive sentences from images using Vision-Language Pre-trained models (VLPs) such as BLIP, which has improved greatly.
no code implementations • 16 Jan 2024 • Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, Jean Oh
Accurate representation in media is known to improve the well-being of the people who consume it.
1 code implementation • 28 Jan 2023 • Zhixuan Liu, Youeun Shin, Beverley-Claire Okogwu, Youngsik Yun, Lia Coleman, Peter Schaldenbrand, Jihie Kim, Jean Oh
It has been shown that accurate representation in media improves the well-being of the people who consume it.
Cultural Vocal Bursts Intensity Prediction Image Generation +3
no code implementations • 21 Aug 2022 • Junghun Kim, Jihie Kim
In particular, we propose a cosine similarity-based graph as an ideal graph structure for representation learning in SER.
no code implementations • 21 Aug 2022 • Junghun Kim, Yoojin An, Jihie Kim
To improve the attention area, we propose to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA) mechanism in combination with the multi-head self-attention.
no code implementations • 21 Aug 2022 • Junghun Kim, Jihie Kim
In this paper, we present a Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations (CMSBERT-CLR), which incorporates the whole context's non-verbal and verbal information and aligns modalities more effectively through contrastive learning.
no code implementations • 27 Jul 2022 • Jinyeong Chae, Jihie Kim
Knowledge-based visual question answering (KVQA) task aims to answer questions that require additional external knowledge as well as an understanding of images and questions.
no code implementations • 23 Jan 2021 • Jinyeong Chae, Ki Yong Hong, Jihie Kim
As there are not enough data to train the deep learning model, we make use of a pretrained model from a relevant domain and data augmentation that is appropriate for this task.
no code implementations • 27 Aug 2019 • Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Yongjin Cho, Sungja Choi, Satish Indurthi, Seunghak Yu, Hyungtak Choi, Inchul Hwang, Jihie Kim
Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.
no code implementations • 27 Aug 2019 • Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Sungja Choi, Inchul Hwang, Jihie Kim
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function.
no code implementations • 2 Dec 2018 • Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee, Jihie Kim
The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way.
no code implementations • WS 2018 • Hyungtak Choi, Siddarth K.M., Haehun Yang, Heesik Jeon, Inchul Hwang, Jihie Kim
In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants.
no code implementations • EMNLP 2018 • Seohyun Back, Seunghak Yu, Sathish Reddy Indurthi, Jihie Kim, Jaegul Choo
Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text.
Ranked #27 on Question Answering on TriviaQA (using extra training data)
1 code implementation • COLING 2018 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.
no code implementations • WS 2017 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes.
1 code implementation • 6 Jul 2017 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.