1 citations · 2 across the 8 of their papers we have counts for
8 papers
ScrewSplat: An End-to-End Method for Articulated Object Recognition
Seungyeon Kim, Junsu Ha, Young Hun Kim +2
Articulated object recognition -- the task of identifying both the geometry and kinematic joints of objects with movable parts -- is essential for enabling robots to interact with…
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Young Hun Kim, Seungyeon Kim, Yonghyeon Lee +1
Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential…
Motion Manifold Flow Primitives for Task-Conditioned Trajectory Generation under Complex Task-Motion Dependencies
Yonghyeon Lee, Byeongho Lee, Seungyeon Kim +1
Effective movement primitives should be capable of encoding and generating a rich repertoire of trajectories -- typically collected from human demonstrations -- conditioned on task…
Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning
Sangwoong Yoon, Dohyun Kwon, Himchan Hwang +2
We present Generalized Contrastive Divergence (GCD), a novel objective function for training an energy-based model (EBM) and a sampler simultaneously. GCD generalizes Contrastive D…
Variational Weighting for Kernel Density Ratios
Sangwoong Yoon, Frank C. Park, Gunsu S Yun +2
Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variatio…
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh +1
We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data. The proposed algorithm, Manifold Project…
On Explicit Curvature Regularization in Deep Generative Models
Yonghyeon Lee, Frank Chongwoo Park
We propose a family of curvature-based regularization terms for deep generative model learning. Explicit coordinate-invariant formulas for both intrinsic and extrinsic curvature me…
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers
Sangwoong Yoon, Jinwon Choi, Yonghyeon Lee +2
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector. Current robustness evaluation protocols for OOD detectors rely on injecting pert…