Search Results for author:
Found 11 papers, 8 papers with code
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
We demonstrate the effectiveness of DiPEx through extensive class-agnostic OD and OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by up to 20. 1% in AR and achieving a 21. 3% AP improvement over SAM.
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection
Specifically, we propose a Model Synergy (MOS) strategy that dynamically selects historical checkpoints with diverse knowledge and assembles them to best accommodate the current test batch.
DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection
We minimize the sharpness to cultivate a flat loss landscape to ensure model resiliency to minor data variations, thereby enhancing the generalization of the adaptation process.
In Search of Lost Online Test-time Adaptation: A Survey
This article presents a comprehensive survey of online test-time adaptation (OTTA), focusing on effectively adapting machine learning models to distributionally different target data upon batch arrival.
Open-CRB: Towards Open World Active Learning for 3D Object Detection
This paper investigates a more practical and challenging research task: Open World Active Learning for 3D Object Detection (OWAL-3D), aimed at acquiring informative point clouds with new concepts.
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.
