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 …

The status quo approach to training object detectors requires expensive bounding box
annotations. Our framework takes a markedly different direction: we transfer tracked object …

Weakly-supervised object detection has attracted much attention lately, since it does not
require bounding box annotations for training. Although significant progress has also been …

Weakly supervised object detection aims at learning precise object detectors, given image
category labels. In recent prevailing works, this problem is generally formulated as a multiple …

This paper addresses weakly supervised object detection with only image-level supervision
at training stage. Previous approaches train detection models with entire images all at once …

In this paper, we propose an effective knowledge transfer framework to boost the weakly
supervised object detection accuracy with the help of an external fully-annotated source …

This paper focuses on the problem of object detection when the annotation at training time is
restricted to presence or absence of object instances at image level. We present a method …

Despite impressive progress in object detection over the last years, it is still an open
challenge to reliably detect objects across visual domains. All current approaches access a …

This paper presents a DETR-based method for cross-domain weakly supervised object
detection (CDWSOD), aiming at adapting the detector from source to target domain through …

Weakly supervised object detection, is a challenging task, where the training procedure
involves learning at the same time both, the model appearance and the object location in …