no code implementations • 8 Mar 2024 • Junsu Kim, Yunhoe Ku, Jihyeon Kim, Junuk Cha, Seungryul Baek
This technique uses Vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training.
no code implementations • 27 Feb 2024 • Junsu Kim, Hoseong Cho, Jihyeon Kim, Yihalem Yimolal Tiruneh, Seungryul Baek
In the field of class incremental learning (CIL), generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models.
Class Incremental Learning Class-Incremental Object Detection +3
no code implementations • 14 Dec 2023 • Junsu Kim, Sumin Hong, Chanwoo Kim, Jihyeon Kim, Yihalem Yimolal Tiruneh, Jeongwan On, Jihyun Song, Sunhwa Choi, Seungryul Baek
In this paper, we introduce an effective buffer training strategy (eBTS) that creates the optimized replay buffer on object detection.
1 code implementation • 20 Mar 2023 • Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son, Jinwoo Shin
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies.
1 code implementation • 5 Feb 2023 • Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel
In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation.
1 code implementation • NeurIPS 2021 • Junsu Kim, Younggyo Seo, Jinwoo Shin
In this paper, we present HIerarchical reinforcement learning Guided by Landmarks (HIGL), a novel framework for training a high-level policy with a reduced action space guided by landmarks, i. e., promising states to explore.
Efficient Exploration Hierarchical Reinforcement Learning +2
no code implementations • 29 Sep 2021 • Kyunghwan Son, Junsu Kim, Yung Yi, Jinwoo Shin
Although these two sources are both important factors for learning robust policies of agents, prior works do not separate them or deal with only a single risk source, which could lead to suboptimal equilibria.
Ranked #1 on SMAC+ on Off_Near_parallel
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 9 Jun 2021 • Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin
Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself.
Ranked #4 on Multi-step retrosynthesis on USPTO-190
2 code implementations • NeurIPS 2020 • Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin
De novo molecular design attempts to search over the chemical space for molecules with the desired property.