Search Results for author:
Found 8 papers, 6 papers with code
In Search of Lost Online Test-time Adaptation: A Survey
In this paper, we present a comprehensive survey on online test-time adaptation (OTTA), a paradigm focused on adapting machine learning models to novel data distributions upon batch arrival.
Towards Open World Active Learning for 3D Object Detection
To seek effective solutions, we investigate a more practical yet challenging research task: Open World Active Learning for 3D Object Detection (OWAL-3D), aiming at selecting a small number of 3D boxes to annotate while maximizing detection performance on both known and unknown classes.
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection.
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations.
Exploring Active 3D Object Detection from a Generalization Perspective
To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance.
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
However, most existing OSDA approaches are limited due to three main reasons, including: (1) the lack of essential theoretical analysis of generalization bound, (2) the reliance on the coexistence of source and target data during adaptation, and (3) failing to accurately estimate the uncertainty of model predictions.
RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing.
Conditional Extreme Value Theory for Open Set Video Domain Adaptation
The majority of video domain adaptation algorithms are proposed for closed-set scenarios in which all the classes are shared among the domains.
