Online Pseudo-Label Unified Object Detection for Multiple Datasets Training
- URL: http://arxiv.org/abs/2410.15569v1
- Date: Mon, 21 Oct 2024 01:23:42 GMT
- Title: Online Pseudo-Label Unified Object Detection for Multiple Datasets Training
- Authors: XiaoJun Tang, Jingru Wang, Zeyu Shangguan, Darun Tang, Yuyu Liu,
- Abstract summary: We propose an Online Pseudo-Label Unified Object Detection scheme.
Our method uses a periodically updated teacher model to generate pseudo-labels for the unlabelled objects in each sub-dataset.
We also propose a category specific box regression and a pseudo-label RPN head to improve the recall rate of the Region Proposal Network (PRN)
- Score: 0.0
- License:
- Abstract: The Unified Object Detection (UOD) task aims to achieve object detection of all merged categories through training on multiple datasets, and is of great significance in comprehensive object detection scenarios. In this paper, we conduct a thorough analysis of the cross datasets missing annotations issue, and propose an Online Pseudo-Label Unified Object Detection scheme. Our method uses a periodically updated teacher model to generate pseudo-labels for the unlabelled objects in each sub-dataset. This periodical update strategy could better ensure that the accuracy of the teacher model reaches the local maxima and maximized the quality of pseudo-labels. In addition, we survey the influence of overlapped region proposals on the accuracy of box regression. We propose a category specific box regression and a pseudo-label RPN head to improve the recall rate of the Region Proposal Network (PRN). Our experimental results on common used benchmarks (\eg COCO, Object365 and OpenImages) indicates that our online pseudo-label UOD method achieves higher accuracy than existing SOTA methods.
Related papers
- Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation [58.37525311718006]
We put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD)
We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario.
Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects.
arXiv Detail & Related papers (2024-11-04T12:59:13Z) - TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework.
We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data.
Our approach improves pseudo-label quality in two distinct manners.
arXiv Detail & Related papers (2024-09-17T05:35:00Z) - SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [59.868772767818975]
We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.
Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation.
Extensive experiments conducted on various multi-oriented object datasets under various labeled settings demonstrate the effectiveness of our method.
arXiv Detail & Related papers (2024-07-01T07:03:51Z) - Improved Region Proposal Network for Enhanced Few-Shot Object Detection [23.871860648919593]
Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches.
We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during the FSOD training stage.
Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception of the object detection model for large objects.
arXiv Detail & Related papers (2023-08-15T02:35:59Z) - Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and
Class-balanced Pseudo-Labeling [38.07637524378327]
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection.
Existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting.
We propose a novel ReDB framework tailored for learning to detect all classes at once.
arXiv Detail & Related papers (2023-07-16T04:34:11Z) - Identification of Novel Classes for Improving Few-Shot Object Detection [12.013345715187285]
Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class.
We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during training to improve FSOD performance.
Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.
arXiv Detail & Related papers (2023-03-18T14:12:52Z) - Semi-Supervised Object Detection with Object-wise Contrastive Learning
and Regression Uncertainty [46.21528260727673]
We propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework.
By jointly filtering the pseudo-labels for the classification and regression heads, the student network receives better guidance from the teacher network for object detection task.
arXiv Detail & Related papers (2022-12-06T04:37:51Z) - Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D
Object Detection [85.11649974840758]
3D object detection networks tend to be biased towards the data they are trained on.
We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
arXiv Detail & Related papers (2021-11-30T18:42:42Z) - ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D
Object Detection [78.71826145162092]
We present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection.
We equip the pseudo label generation process with a hybrid quality-aware triplet memory to improve the quality and stability of generated pseudo labels.
In the model training stage, we propose a source data assisted training strategy and a curriculum data augmentation policy.
arXiv Detail & Related papers (2021-08-15T07:49:06Z) - Salient Objects in Clutter [130.63976772770368]
This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets.
This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.
We propose a new high-quality dataset and update the previous saliency benchmark.
arXiv Detail & Related papers (2021-05-07T03:49:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.