108 citations · 198 across the 17 of their papers we have counts for
6 papers · 1 filter
Fine-Grained Neural Architecture Search
Heewon Kim, Seokil Hong, Bohyung Han +2
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even gene…
PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose
Ju Yong Chang, Gyeongsik Moon, Kyoung Mu Lee
This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute…
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly prop…
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Dongmin Park, Seokil Hong, Bohyung Han +1
Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suf…
Learning to Forget for Meta-Learning
Sungyong Baik, Seokil Hong, Kyoung Mu Lee
Few-shot learning is a challenging problem where the goal is to achieve generalization from only few examples. Model-agnostic meta-learning (MAML) tackles the problem by formulatin…
Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection. In state-of-the-art top-down methods, multi…