Weakly Supervised Object Detection
36 papers with code • 15 benchmarks • 12 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 )
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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.
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.
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
The additive model encourages the predicted object region to be supported by its surrounding context region.