Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.
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Can we detect common objects in a variety of image domains without instance-level annotations?
SOTA for Weakly Supervised Object Detection on Watercolor2k (using extra training data)
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
#2 best model for Weakly Supervised Object Detection on COCO
The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i. e., without object location information.
#4 best model for Weakly Supervised Object Detection on ImageNet
Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.
#19 best model for Weakly Supervised Object Detection on PASCAL VOC 2007
The additive model encourages the predicted object region to be supported by its surrounding context region.
#18 best model for Weakly Supervised Object Detection on PASCAL VOC 2012 test
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.
#2 best model for Weakly Supervised Object Detection on PASCAL VOC 2007
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.
#10 best model for Weakly Supervised Object Detection on PASCAL VOC 2007
Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student.