1.8k citations · 2.8k across the 12 of their papers we have counts for
12 papers
One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching
Siyuan Yang, Jun Liu, Shijian Lu +2
One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge…
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization
Siyuan Yang, Jun Liu, Shijian Lu +3
3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large numbe…
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve
Lei Wang, Jun Liu, Piotr Koniusz
In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignmen…
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
Siyuan Yang, Jun Liu, Shijian Lu +2
Skeleton-based human action recognition has attracted increasing attention in recent years. However, most of the existing works focus on supervised learning which requiring a large…
Conterfactual Generative Zero-Shot Semantic Segmentation
Feihong Shen, Jun Liu, Ping Hu
zero-shot learning is an essential part of computer vision. As a classical downstream task, zero-shot semantic segmentation has been studied because of its applicant value. One of…
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
Xiaoli Liu, Jianqin Yin, Jin Liu +3
Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new 2D CNN based network, TrajectoryNet, to predict future…