ScaleDet: A Scalable Multi-Dataset Object Detector

Chen, Yanbei; Wang, Manchen; Mittal, Abhay; Xu, Zhenlin; Favaro, Paolo; Tighe, Joseph; Modolo, Davide (2023). ScaleDet: A Scalable Multi-Dataset Object Detector. In: International Conference on Computer Vision and Pattern Recognition.

Text Chen_ScaleDet_A_Scalable_Multi-Dataset_Object_Detector_CVPR_2023_paper.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (2MB) | Request a copy

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone

Interest & Impact

Downloads

0 since deposited on 23 May 2024
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item