60 citations · 244 across the 18 of their papers we have counts for
14 papers · 1 filter
Parse Graph-Based Visual-Language Interaction for Human Pose Estimation
Shibang Liu, Xuemei Xie, Guangming Shi
Parse graphs boost human pose estimation (HPE) by integrating context and hierarchies, yet prior work mostly focuses on single modality modeling, ignoring the potential of multimod…
KB-DMGen: Knowledge-Based Global Guidance and Dynamic Pose Masking for Human Image Generation
Shibang Liu, Xuemei Xie, Guangming Shi
Recent methods using diffusion models have made significant progress in Human Image Generation (HIG) with various control signals such as pose priors. In HIG, both accurate human p…
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
Mingjie Wei, Xuemei Xie, Yutong Zhong +1
Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dep…
ACMo: Attribute Controllable Motion Generation
Mingjie Wei, Xuemei Xie, Guangming Shi
Attributes such as style, fine-grained text, and trajectory are specific conditions for describing motion. However, existing methods often lack precise user control over motion att…
Refinement Module based on Parse Graph for Human Pose Estimation
Shibang Liu, Xuemei Xie
Parse graphs have been widely used in Human Pose Estimation (HPE) to model the hierarchical structure and context relations of the human body. However, such methods often suffer fr…
Learning Primitive-aware Discriminative Representations for Few-shot Learning
Jianpeng Yang, Yuhang Niu, Xuemei Xie +1
Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to recognize novel classes with only a few labeled examples. Some recent work about FSL has yielded pr…
Temporal Graph Modeling for Skeleton-based Action Recognition
Jianan Li, Xuemei Xie, Zhifu Zhao +3
Graph Convolutional Networks (GCNs), which model skeleton data as graphs, have obtained remarkable performance for skeleton-based action recognition. Particularly, the temporal dyn…
Full Image Recover for Block-Based Compressive Sensing
Xuemei Xie, Chenye Wang, Jiang Du +1
Recent years, compressive sensing (CS) has improved greatly for the application of deep learning technology. For convenience, the input image is usually measured and reconstructed…
Perceptual Compressive Sensing
Jiang Du, Xuemei Xie, Chenye Wang +1
Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This ca…
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Xiaotong Lu, Weisheng Dong, Peiyao Wang +2
Compressive sensing (CS), aiming to reconstruct an image/signal from a small set of random measurements has attracted considerable attentions in recent years. Due to the high dimen…
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Jiang Du, Xuemei Xie, Chenye Wang +3
Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. In the existing methods, the scene is measured block by block…
Real-Time Illegal Parking Detection System Based on Deep Learning
Xuemei Xie, Chenye Wang, Shu Chen +2
The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this met…
Adaptive Measurement Network for CS Image Reconstruction
Xuemei Xie, Yuxiang Wang, Guangming Shi +3
Conventional compressive sensing (CS) reconstruction is very slow for its characteristic of solving an optimization problem. Convolu- tional neural network can realize fast process…
Feature-Fused SSD: Fast Detection for Small Objects
Guimei Cao, Xuemei Xie, Wenzhe Yang +3
Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sac…