We test the effectiveness our PST2 with two different tasks on point cloud sequences, i. e., 4D semantic segmentation and 3D action recognition.
no code implementations • 18 Aug 2021 • Haoran Peng, He Huang, Li Xu, Tianjiao Li, Jun Liu, Hossein Rahmani, Qiuhong Ke, Zhicheng Guo, Cong Wu, Rongchang Li, Mang Ye, Jiahao Wang, Jiaxu Zhang, Yuanzhong Liu, Tao He, Fuwei Zhang, Xianbin Liu, Tao Lin
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021.
A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image.
Experimental results show that compared with the state-of-the-art models, which imitate hexagonal processing but using rectangle-shaped filters, HexCNN reduces the training time by up to 42. 2%.
Understanding the actions of both humans and artificial intelligence (AI) agents is important before modern AI systems can be fully integrated into our daily life.
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action.
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.
With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities.
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes.
Ranked #4 on Skeleton Based Action Recognition on SYSU 3D
This paper presents a new representation of skeleton sequences for 3D action recognition.
Ranked #26 on Skeleton Based Action Recognition on NTU RGB+D 120
Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition.
Ranked #73 on Skeleton Based Action Recognition on NTU RGB+D
This paper presents a new method for 3D action recognition with skeleton sequences (i. e., 3D trajectories of human skeleton joints).
Ranked #30 on Skeleton Based Action Recognition on NTU RGB+D 120
The structural models, including the spatial and the temporal models, are learned with Long Short Term Memory (LSTM) networks to capture the dependency of the global and local contexts of each RGB frame and each optical flow image, respectively.