hyolim's site

Hyolim Kang

I am a Ph.D. student studying computer science at Yonsei University, where I am advised by Professor Seon Joo Kim.

My primary research areas are computer vision and deep learning, with specific interests in video understanding, perception for action, vision for robotics, and representation learning.

CV / LinkedIn / GitHub

I am actively looking for internship positions!

Education

Ph.D candidate in Computer Science, Yonsei University, Seoul, Korea [Mar 2020 - Feb 2025 (expected)] Advisor: Seon Joo Kim
B.S in Computer Science, Yonsei University, Seoul, Korea [Mar 2014 - Feb 2020]
GPA 3.85/4.3 -- Top 8% in Dept. of Computer Science

Research Objective

My main research direction revolves around the challenge of efficient streaming perception in embodied AI and robotics. This area is vital because, unlike traditional models, embodied agents often lack complete observational data, necessitating real-time, sequential decision-making based on partial, streaming inputs. This mirrors how humans process visual information continuously, not in isolated instances.

I am currently excited about developing a "generic visual streaming encoder" adaptable for various streaming perception tasks in computer vision. I question the current practice of independent tokenization, where short clips are processed independently in pretrained clip encoders. My aim is to develop a contextual video clip embedding model that adaptively extracts clip features within their context, using architectures like RNN or S4. This approach mirrors the evolution in NLP from static word embeddings like word2vec to dynamic, context-aware models such as BERT and ELMo.

You can assess my full research statement here.