Wonjun Jo

I am a Ph.D. candidate in the Department of Electrical Engineering at POSTECH. I conduct my research at the Advanced Machine Intelligence (AMI) Lab at KAIST, advised by Prof. Tae-Hyun Oh.

I am passionate about researching Embodied AI for real-world robotics.
My work focuses on label-efficient learning and multi-modal integration, with core interests in Self-supervised Learning, Multi-modal Learning, and World Models.
I also study these problems through an information-theoretic perspective.

Recently, I am interested in world models for robotics using video data and active search, and also interested in tactile sensors for a more comprehensive understanding of the world.

Embodied AI Self-supervised Learning Multi-modal Learning World Models

Email | CV | Scholar | LinkedIn

Research

	LiDAR-Anchored Collaborative Distillation for Robust 2D Representations Wonjun Jo, Hyunwoo Ha, Kim Ji-Yeon, Hawook Jeong, Tae-Hyun Oh Under-review Project Page \| Paper Improving robustness of self-supervised 2D representation learning.
	DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun Oh Under-review Project Page \| Paper Benchmarking the robustness of Vision-Language Models in low-light environments.
	Self-Supervised Collaborative Distillation: Enhancing Lighting Robustness and 3D Awareness Wonjun Jo, Hyunwoo Ha, Kim Ji-Yeon, Hawook Jeong, Tae-Hyun Oh Workshop on Wild3D, ICCV, 2025 Paper Improving pre-trained 2D image encoder's lighting robustness and 3D awareness.
	The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning Wonjun Jo, Kwon Byung-Ki, Kim Ji-Yeon, Hawook Jeong, Kyungdoon Joo, Tae-Hyun Oh ACCV, 2024 Project Page \| Paper Proposing simple remedies for self-supervised 3D representation learning with 2D representation.

Notes

Research Core

Self-Supervised Learning

Multi-Modal Learning

World Models

Embodied AI

Energy-Based Models (Yann LeCun)

Free Energy Principle (Karl Friston)

Music

No Time for Caution Hans Zimmer YouTube
Can You Hear The Music Ludwig Goransson YouTube
Time Hans Zimmer YouTube
We Have to Go Steve Jablonsky YouTube
Cloud Atlas End Title Tom Tykwer, MDR Rundfunkchor, MDR Sinfonieorchester YouTube
Tears in the Rain Hans Zimmer, Benjamin Wallfisch YouTube
Seven Worlds One Planet Suite Hans Zimmer YouTube
Contact - End Credits Alan Silvestri YouTube
Transformation (End Titles) Hans Zimmer YouTube
Leaving Caladan Hans Zimmer YouTube
You Don't Have To Kris Bowers YouTube
F1 Hans Zimmer YouTube