6 papers
Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks
Changgyoon Oh, Jongoh Jeong, Jegyeong Cho +1
Denoising diffusion probabilistic models have brought tremendous advances in generative tasks, achieving state-of-the-art performance thus far. Current diffusion model-based applic…
AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding
Jongoh Jeong, Taek-Jin Song, Jong-Hwan Kim +1
Understanding road scenes for visual perception remains crucial for intelligent self-driving cars. In particular, it is desirable to detect unexpected small road hazards reliably i…
Exploring Syn-to-Real Domain Adaptation for Military Target Detection
Jongoh Jeong, Youngjin Oh, Gyeongrae Nam +2
Object detection is one of the key target tasks of interest in the context of civil and military applications. In particular, the real-world deployment of target detection methods…
Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model
Daehee Park, Monu Surana, Pranav Desai +3
While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works address…
Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
Giwon Lee, Wooseong Jeong, Daehee Park +2
Motion planning is a crucial component of autonomous robot driving. While various trajectory datasets exist, effectively utilizing them for a target domain remains challenging due…
Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
Giwon Lee, Daehee Park, Jaewoo Jeong +1
Safe and effective motion planning is crucial for autonomous robots. Diffusion models excel at capturing complex agent interactions, a fundamental aspect of decision-making in dyna…