고형진 교수
Hyung-Jin Ko
성균관대학교 소비자학과 · 경제학
연구실 소개
고형진 교수의 연구실은 비디오-언어 이해 분야에서 고수준 개념어를 자동으로 탐지하고, 이를 언어 생성 모델에 효과적으로 통합하는 기반 기술을 개발하고 있습니다. 특히 외부 지식 사전 없이도 엔드 투 엔드로 학습 가능한 개념어 검출기와 의미적 어텐션 메커니즘을 통해 비디오 이해의 정확성과 해석 능력을 향상시키는 데 초점을 맞추고 있습니다. 또한 디지털 자산, 특히 DeFi 및 NFT 시장의 자산 가격 움직임과 시장 안정성에 대한 경제적 분석을 통해 금융시장에서의 헤지 및 안전자산 기능을 규명하고 있습니다. 이와 더불어 실시간 데이터 변화에 대응할 수 있는 온라인 앙상블 딥러닝 알고리즘 개발을 통해 지속가능하고 견고한 AI 시스템을 구축하는 데에도 기여하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To effectively exploit the det
We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To maximize the values of dete
This study conducted the econometric analysis to test the hedge and safe haven effects of Non-fungible Tokens (NFTs) on major traditional asset markets in the global financial system. We investigate the estimates of these effects in times of extreme market conditions and the COVID-19 crisis. Our empirical results show evidence of the hedge and safe haven properties of NFTs, confirming two main findings: (i) NFTs act as a hedge and safe haven for particular stock markets and oil, bond, and USD in
We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To maximize the values of dete
In decentralized finance (Defi), market participants are allowed to have the right to manage their own funds as opposed to centralized finance (Cefi) with a central custodian, centralized exchanges (CEX). Most Defi projects provide their own service and simultaneously issue a unique token that can be traded in decentralized exchanges (DEX). However, the values of these tokens have rarely been studied. We confirm that the prices of tokens in the Defi market have a persistent tendency to move toge
Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and
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