4 papers
Learning to Orchestrate Vision Foundation Models for Multi-Task Dense Prediction
Donghyun Han, Yuseok Bae, Jung Uk Kim +1
Vision foundation models (VFMs) exhibit complementary strengths shaped by their pretraining objectives. Yet prevailing methods for multi-task dense prediction still train an entire…
Task Prototype-Based Knowledge Retrieval for Multi-Task Learning from Partially Annotated Data
Youngmin Oh, Hyung-Il Kim, Jung Uk Kim
Multi-task learning (MTL) is critical in real-world applications such as autonomous driving and robotics, enabling simultaneous handling of diverse tasks. However, obtaining fully…
Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning
Hyung-Il Kim, Kimin Yun, Jun-Seok Yun +1
Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer…
MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection
Youngmin Oh, Hyung-Il Kim, Seong Tae Kim +1
Monocular 3D object detection is an important challenging task in autonomous driving. Existing methods mainly focus on performing 3D detection in ideal weather conditions, characte…