Author name cluster

Changjae Oh

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Coming Out of the Dark: Human Pose Estimation in Low-light Conditions

Yong Su
Defang Chen
Meng Xing
Changjae Oh
Xuewei Liu
Jieyang Li

Human pose estimation in low-light conditions is vital for applications such as surveillance and autonomous systems, yet the severe visual distortions hinder both manual annotation and estimation precision. Existing approaches typically rely on additional reference information to mitigate these issues, however, customized data collection equipment poses limitations on their scalability. To alleviate the issue, we construct a Low-Light Images and Poses (LLIP) dataset, which includes only paired low-light images and pose annotations obtained using off-the-shelf motion capture devices. Furthermore, we propose a Multi-grained High-frequency Feature Consistency Learning framework (MHFCL), which does not rely on additional reference information. MHFCL employs a Retinex-inspired restoration stream to recover high-frequency details and integrates them into pose estimation using a multi-grained consistency mechanism. Experiments demonstrate that our approach achieves a new benchmark in low-light pose estimation, while maintaining competitive performance in well-lit conditions.

PDF Details DOI

IROS Conference 2025 Conference Paper

Improving Generalization of Language-Conditioned Robot Manipulation

Chenglin Cui
Chaoran Zhu
Changjae Oh
Andrea Cavallaro

The control of robots for manipulation tasks generally relies on visual input. Recent advances in vision-language models (VLMs) enable the use of natural language instructions to condition visual input and control robots in a wider range of environments. However, existing methods require a large amount of data to fine-tune VLMs for operating in unseen environments. In this paper, we present a framework that learns object-arrangement tasks from just a few demonstrations. We propose a two-stage framework that divides object-arrangement tasks into a target localization stage, for picking the object, and a region determination stage for placing the object. We present an instance-level semantic fusion module that aligns the instance-level image crops with the text embedding, enabling the model to identify the target objects defined by the natural language instructions. We validate our method on both simulation and real-world robotic environments. Our method, fine-tuned with a few demonstrations, improves generalization capability and demonstrates zero-shot ability in real-robot manipulation scenarios.

Details

AAAI Conference 2024 Conference Paper

Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection

Meng Xing
Zhiyong Feng
Yong Su
Changjae Oh

Detecting OOD inputs is crucial to deploy machine learning models to the real world safely. However, existing OOD detection methods require an in-distribution (ID) dataset to retrain the models. In this paper, we propose a Deep Generative Models (DGMs) based transferable OOD detection that does not require retraining on the new ID dataset. We first establish and substantiate two hypotheses on DGMs: DGMs exhibit a predisposition towards acquiring low-level features, in preference to semantic information; the lower bound of DGM's log-likelihoods is tied to the conditional entropy between the model input and target output. Drawing on the aforementioned hypotheses, we present an innovative image-erasing strategy, which is designed to create distinct conditional entropy distributions for each individual ID dataset. By training a DGM on a complex dataset with the proposed image-erasing strategy, the DGM could capture the discrepancy of conditional entropy distribution for varying ID datasets, without re-training. We validate the proposed method on the five datasets and show that, without retraining, our method achieves comparable performance to the state-of-the-art group-based OOD detection methods. The project codes will be open-sourced on our project website.

PDF Details DOI

IROS Conference 2021 Conference Paper

OHPL: One-shot Hand-eye Policy Learner

Changjae Oh
Yik Lung Pang
Andrea Cavallaro

The control of a robot for manipulation tasks generally relies on object detection and pose estimation. An attractive alternative is to learn control policies directly from raw input data. However, this approach is time-consuming and expensive since learning the policy requires many trials with robot actions in the physical environment. To reduce the training cost, the policy can be learned in simulation with a large set of synthetic images. The limit of this approach is the domain gap between the simulation and the robot workspace. In this paper, we propose to learn a policy for robot reaching movements from a single image captured directly in the robot workspace from a camera placed on the end-effector (a hand-eye camera). The idea behind the proposed policy learner is that view changes seen from the hand-eye camera produced by actions in the robot workspace are analogous to locating a region-of-interest in a single image by performing sequential object localisation. This similar view change enables training of object reaching policies using reinforcement-learning-based sequential object localisation. To facilitate the adaptation of the policy to view changes in the robot workspace, we further present a dynamic filter that learns to bias an input state to remove irrelevant information for an action decision. The proposed policy learner can be used as a powerful representation for robotic tasks, and we validate it on static and moving object reaching tasks.

Details

ICRA Conference 2019 Conference Paper

Learning Action Representations for Self-supervised Visual Exploration

Changjae Oh
Andrea Cavallaro

Learning to efficiently navigate an environment using only an on-board camera is a difficult task for an agent when the final goal is far from the initial state and extrinsic rewards are sparse. To address this problem, we present a self-supervised prediction network to train the agent with intrinsic rewards that relate to achieving the desired final goal. The network learns to predict its future camera view (the future state) from a current state-action pair through an Action Representation Module that decodes input actions as higher dimensional representations. To increase the representational power of the network during exploration we fuse the responses from the Action Representation Module in the transition network, which predicts the future state. Moreover, to enhance the discrimination capability between predictions from different input actions we introduce joint regression and triplet ranking loss functions. We show that, despite the sparse extrinsic rewards, by learning action representations we achieve a faster training convergence than state-of-the-art methods with only a small increase in the number of the model parameters.

Details