26 papers with code • 15 benchmarks • 9 datasets
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 )
Can we detect common objects in a variety of image domains without instance-level annotations?
Ranked #2 on Weakly Supervised Object Detection on Watercolor2k (using extra training data)
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Ranked #1 on Weakly Supervised Object Detection on COCO test-dev
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Ranked #3 on Weakly Supervised Object Detection on COCO
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
Ranked #2 on 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.
Ranked #1 on Weakly Supervised Object Detection on ImageNet
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.
Ranked #4 on Weakly Supervised Object Detection on ImageNet
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.
Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.
Ranked #3 on Weakly Supervised Object Detection on HICO-DET
Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline.
The additive model encourages the predicted object region to be supported by its surrounding context region.
Ranked #4 on Weakly Supervised Object Detection on Charades