10 citations · 17 across the 6 of their papers we have counts for
6 papers
DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning
Zhimin Chen, Bing Li
Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investiga…
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…
End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving
Satya R. Jaladi, Zhimin Chen, Narahari R. Malayanur +2
The current autonomous stack is well modularized and consists of perception, decision making and control in a handcrafted framework. With the advances in artificial intelligence (A…
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…
Class-Level Confidence Based 3D Semi-Supervised Learning
Zhimin Chen, Longlong Jing, Liang Yang +2
Recent state-of-the-art method FlexMatch firstly demonstrated that correctly estimating learning status is crucial for semi-supervised learning (SSL). However, the estimation metho…
Multimodal Semi-Supervised Learning for 3D Objects
Zhimin Chen, Longlong Jing, Yang Liang +2
In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D task…