Search Results for author:
Found 10 papers, 4 papers with code
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer
Subsequently, we develop a mixed transformer architecture to incorporate spatial features with frequency features to model the comprehensive frequency-spatial patterns.
Adversary-Guided Motion Retargeting for Skeleton Anonymization
Skeleton-based motion visualization is a rising field in computer vision, especially in the case of virtual reality (VR).
What User Behaviors Make the Differences During the Process of Visual Analytics?
This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods.
Part Aware Contrastive Learning for Self-Supervised Action Recognition
This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations.
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition
Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task.
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh.
Ranked #68 on
3D Human Pose Estimation
on 3DPW
Poisoning Attacks on Fair Machine Learning
Our framework enables attackers to flexibly adjust the attack's focus on prediction accuracy or fairness and accurately quantify the impact of each candidate point to both accuracy loss and fairness violation, thus producing effective poisoning samples.
One-Class Adversarial Nets for Fraud Detection
Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.
Spectrum-based deep neural networks for fraud detection
Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible.
On Spectral Analysis of Directed Signed Graphs
It has been shown that the adjacency eigenspace of a network contains key information of its underlying structure.
