Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

2018-03-30 · CVPR 2018 6 · Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, Kiyoharu Aizawa

Can we detect common objects in a variety of image domains without instance-level annotations? In this paper, we present a framework for a novel task, cross-domain weakly supervised object detection, which addresses this question. For this paper, we have access to images with instance-level annotations in a source domain (e.g., natural image) and images with image-level annotations in a target domain (e.g., watercolor). In addition, the classes to be detected in the target domain are all or a subset of those in the source domain. Starting from a fully supervised object detector, which is pre-trained on the source domain, we propose a two-step progressive domain adaptation technique by fine-tuning the detector on two types of artificially and automatically generated samples. We test our methods on our newly collected datasets containing three image domains, and achieve an improvement of approximately 5 to 20 percentage points in terms of mean average precision (mAP) compared to the best-performing baselines.

📄 PDF Abstract BibTeX arXiv:1803.11365

Code (3)

naoto0804/cross-domain-detection 공식 구현

barisbatuhan/dass_det_inference pytorch

barisbatuhan/dass_detector pytorch

Tasks

Domain Adaptation object-detection Object Detection Weakly Supervised Object Detection

D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation

2022-12-02 · Yuting Wang, Ricardo Guerrero, Vladimir Pavlovic

Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and …

Domain Adaptation Object object-detection Object Detection+2

Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

2021-06-19 · CVPR 2021 1 · Luwei Hou, Yu Zhang, Kui Fu, Jia Li

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e. with object bounding boxes) to train object detectors for target domains that are …

Object object-detection Object Detection Transfer Learning+1

Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection

2021-08-23 · IEEE International Conference on Image Processing (ICIP) 2021 8 · Shengxiong Ouyang, Xinglu Wang, Kejie Lyu, Yingming Li

Cross domain weakly supervised object detection (CDWSOD), where we can get access to instance-level annotations in the source domain while only image-level annotations are available in the target domain, adapts object de…

object-detection Object Detection Pseudo Label Weakly Supervised Object Detection

Seeking Flat Minima with Mean Teacher on Semi- and Weakly-Supervised Domain Generalization for Object Detection

2023-10-30 · Ryosuke Furuta, Yoichi Sato

Object detectors do not work well when domains largely differ between training and testing data. To overcome this domain gap in object detection without requiring expensive annotations, we consider two problem settings: …

Domain Adaptation Domain Generalization Object object-detection+2

H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection

2022-01-01 · CVPR 2022 1 · Yunqiu Xu, Yifan Sun, Zongxin Yang, Jiaxu Miao 외

Cross-domain weakly supervised object detection (CDWSOD) aims to adapt the detection model to a novel target domain with easily acquired image-level annotations. How to align the source and target domains is critical…