62 citations · 99 across the 16 of their papers we have counts for
16 papers
Source-free Domain Adaptive Human Pose Estimation
Qucheng Peng, Ce Zheng, Chen Chen
Human Pose Estimation (HPE) is widely used in various fields, including motion analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world datasets…
LAMP: Leveraging Language Prompts for Multi-person Pose Estimation
Shengnan Hu, Ce Zheng, Zixiang Zhou +2
Human-centric visual understanding is an important desideratum for effective human-robot interaction. In order to navigate crowded public places, social robots must be able to inte…
Part Aware Contrastive Learning for Self-Supervised Action Recognition
Yilei Hua, Wenhan Wu, Ce Zheng +4
In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the seman…
PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation
Qitao Zhao, Ce Zheng, Mengyuan Liu +2
Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relation…
POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery
Ce Zheng, Xianpeng Liu, Guo-Jun Qi +1
Transformer architectures have achieved SOTA performance on the human mesh recovery (HMR) from monocular images. However, the performance gain has come at the cost of substantial m…
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition
Andong Deng, Taojiannan Yang, Chen Chen
The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair evaluation and thus facilitate the evolution of a specific area. Nonetheless, we poin…
DiffMesh: A Motion-aware Diffusion Framework for Human Mesh Recovery from Videos
Ce Zheng, Xianpeng Liu, Qucheng Peng +3
Human mesh recovery (HMR) provides rich human body information for various real-world applications. While image-based HMR methods have achieved impressive results, they often strug…
AIM: Adapting Image Models for Efficient Video Action Recognition
Taojiannan Yang, Yi Zhu, Yusheng Xie +3
Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However,…
Revisiting Training-free NAS Metrics: An Efficient Training-based Method
Taojiannan Yang, Linjie Yang, Xiaojie Jin +1
Recent neural architecture search (NAS) works proposed training-free metrics to rank networks which largely reduced the search cost in NAS. In this paper, we revisit these training…
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Guangyu Sun, Umar Khalid, Matias Mendieta +2
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the ty…
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition
Wenhan Wu, Yilei Hua, Ce Zheng +3
Fully supervised skeleton-based action recognition has achieved great progress with the blooming of deep learning techniques. However, these methods require sufficient labeled data…
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
Ce Zheng, Matias Mendieta, Taojiannan Yang +2
Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and huma…
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
Ce Zheng, Matias Mendieta, Chen Chen
Facial expression recognition (FER) is an important task in computer vision, having practical applications in areas such as human-computer interaction, education, healthcare, and o…
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
Ce Zheng, Matias Mendieta, Pu Wang +2
Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model s…
3D Human Pose Estimation with Spatial and Temporal Transformers
Ce Zheng, Sijie Zhu, Matias Mendieta +3
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, objec…
HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology
Weiguo Zhou, Xin Jiang, Chen Chen +2
Thanks to the rapid development of CNNs and depth sensors, great progress has been made in 3D hand pose estimation. Nevertheless, it is still far from being solved for its cluttere…