activity
20172024
most citedAIM: Adapting Image Models for Efficient Video Action Recognition

62 citations · 240 across the 28 of their papers we have counts for

collaborators
Showing 2023Show all

14 papers · 1 filter

cs.CV20238 cited

A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation

Qitao Zhao, Ce Zheng, Mengyuan Liu +1

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for…

eess.IV2023

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Haoran Shen, Yifu Zhang, Wenxuan Wang +4

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise c…

cs.CV20231 cited

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

Lijun Li, Linrui Tian, Xindi Zhang +4

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result i…

cs.CV202312 cited

Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning

Yang Liu, Chen Chen, Can Wang +2

Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods still hav…

cs.CV2023

Multi-view Self-supervised Disentanglement for General Image Denoising

Hao Chen, Chenyuan Qu, Yu Zhang +2

With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen…

cs.CV20233 cited

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…