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Jungchan Cho

5 accepted papers

2026

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments

RA-L 2026

Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substan

Cited by 0SourceScholar
2026

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments

ICRA 2026poster

Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substan…

2024

Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding

AAAI 2024technical

In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by the sentence query. To enhance the expression ability of a proposal, we propose a Gaussian mixture proposal (GMP) that c…

2022

Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations

AAAI 2022technical

We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map,…

Cited by 2SourcePDFScholar