Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.
( Image credit: Soft Proposal Networks for Weakly Supervised Object Localization )
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With the recent progress of deep learning, advanced industrial object detectors are built for smart industrial applications.
Ranked #16 on Weakly Supervised Object Detection on PASCAL VOC 2007
Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
Ranked #1 on Weakly Supervised Object Detection on PeopleArt
In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations.
Finally, we fuse these CAMs together to generate pseudoground-truths and train a fully-supervised object detector withthese ground-truths.
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.
Ranked #20 on Weakly Supervised Object Detection on PASCAL VOC 2012 test (using extra training data)
It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations.
To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it.
Ranked #11 on Weakly Supervised Object Detection on PASCAL VOC 2007
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations.
Ranked #23 on Weakly Supervised Object Detection on PASCAL VOC 2007