activity
20232026
most citedBridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models

6 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

Long Jing, Zhixiong Yang, Yajun Zhang +1

Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of multi-source sensors to captu…

cs.LG2026

Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing

Zhixiong Yang, Long Jing, Yao Li +3

AIGC has shown remarkable success in CV and NLP, and has recently demonstrated promising potential in the wireless domain. However, significant data imbalance exists across RF moda…

cs.CV2024

SAM-Guided Masked Token Prediction for 3D Scene Understanding

Zhimin Chen, Liang Yang, Yingwei Li +2

Foundation models have significantly enhanced 2D task performance, and recent works like Bridge3D have successfully applied these models to improve 3D scene understanding through k…

cs.CV2023★ 2 cited

Point Cloud Self-supervised Learning via 3D to Multi-view Masked Learner

Zhimin Chen, Xuewei Chen, Xiao Guo +4

Recently, multi-modal masked autoencoders (MAE) has been introduced in 3D self-supervised learning, offering enhanced feature learning by leveraging both 2D and 3D data to capture…

cs.CV2023★ 6 cited

Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models

Zhimin Chen, Longlong Jing, Yingwei Li +1

Foundation models have achieved remarkable results in 2D and language tasks like image segmentation, object detection, and visual-language understanding. However, their potential t…