Title
Tell Me What They'Re Holding: Weakly-Supervised Object Detection With Transferable Knowledge From Human-Object Interaction
Abstract
In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervision of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to the object detection but also to other domains such as semantic segmentation. The experimental results on HICO-DET dataset show the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects on HICO-DET and V-COCO datasets.
Year
DOI
Venue
2020
10.1609/AAAI.V34I07.6784
AAAI
DocType
Volume
Issue
Conference
34
07
ISSN
Citations 
PageRank 
2159-5399
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Daesik Kim123.15
Gyujeong Lee200.68
Jisoo Jeong352.08
Nojun Kwak486263.79