Publications

My research aims to develop models that understand temporal dynamics like humans. I design neural networks that recognize objects, actions, and interactions in videos, training them to understand how these elements evolve over time—often with language supervision.

You can also find my papers on my Google Scholar profile.

Selected Publications

Leveraging Temporal Contextualization for Video Action Recognition
Minji Kim, Dongyoon Han, Taekyung Kim, Bohyung Han
ECCV 2024

Towards Sequence-Level Training for Visual Tracking
Minji Kim*, Seungkwan Lee*, Jungseul Ok, Bohyung Han, Minsu Cho (*Equal Contribution)
ECCV 2022

Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking
Ilchae Jung, Minji Kim, Eunhyeok Park, Bohyung Han
IJCAI 2022

Other Projects

Learning Gaussian Models for Orientation-Aware Visual Tracking
Minji Kim, Bohyung Han
US Patent (App. 18/370,531)

Spatio-Temporal Modeling via Adaptive Frequency Filtering for Video Action Recognition
Minji Kim, Taehoon Kim, Jonghyeon Seon, Bohyung Han
Journal of Korean Institute of Information Scientists and Engineers (KIISE), 2024

Ensemble Modeling with Convolutional Neural Networks for Application in Visual Object Tracking
Minji Kim, Ilchae Jung, Bohyung Han
Journal of Korean Institute of Information Scientists and Engineers (KIISE), 2021

Top-down Thermal Tracking Based on Rotatable Elliptical Motion Model for Intelligent Livestock Breeding
Minji Kim, Wonjun Kim
Multimedia Systems, Springer, 2020