4 citations · 13 across the 10 of their papers we have counts for
6 papers · 1 filter
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Yaohua Zha, Huizhen Ji, Jinmin Li +5
Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifica…
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
Hao Fang, Bin Chen, Xuan Wang +2
Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradient…
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
Hang Guo, Tao Dai, Mingyan Zhu +4
Recognizing characters from low-resolution (LR) text images poses a significant challenge due to the information deficiency as well as the noise and blur in low-quality images. Cur…
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding
Taolin Zhang, Sunan He, Dai Tao +3
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improve…
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Yaohua Zha, Jinpeng Wang, Tao Dai +3
Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of ful…
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers
Mingqing Wang, Jiawei Li, Zhenyang Li +4
Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in dise…