Search Results for author:
Found 15 papers, 10 papers with code
Explore Human Parsing Modality for Action Recognition
Multimodal-based action recognition methods have achieved high success using pose and RGB modality.
Ranked #5 on
Action Recognition
on NTU RGB+D 120
Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video
Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging.
Integrating Human Parsing and Pose Network for Human Action Recognition
We propose an Integrating Human Parsing and Pose Network (IPP-Net) for action recognition, which is the first to leverage both skeletons and human parsing feature maps in dual-branch approach.
Ranked #8 on
Action Recognition
on NTU RGB+D 120
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
It follows the unsupervised setting and only normal (defect-free) images are used for training.
Edge-guided Representation Learning for Underwater Object Detection
The key to addressing these challenges is to focus the model on obtaining more discriminative information.
Interweaved Graph and Attention Network for 3D Human Pose Estimation
Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of human skeleton representations.
Ranked #155 on
3D Human Pose Estimation
on Human3.6M
GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose
3D human mesh recovery from a 2D pose plays an important role in various applications.
Ranked #165 on
3D Human Pose Estimation
on Human3.6M
HTNet: Human Topology Aware Network for 3D Human Pose Estimation
3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs.
Ranked #39 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed.
Contrastive Learning
Few-Shot Skeleton-Based Action Recognition
+5
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network
Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation.
Monocular 3D Human Pose Estimation
Weakly-supervised 3D Human Pose Estimation
+1
Cloth Interactive Transformer for Virtual Try-On
In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask.
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning
Second, for the images with the same semantic content in different domains, their hidden features should be equivalent.
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
The modified VTE is termed as Strided Transformer Encoder (STE), which is built upon the outputs of VTE.
Ranked #2 on
3D Human Pose Estimation
on HumanEva-I
WQT and DG-YOLO: towards domain generalization in underwater object detection
This paper aims to build a GUOD with small underwater dataset with limited types of water quality.
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
To the best of our knowledge, this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.
