Weakly Supervised Object Detection
50 papers with code • 17 benchmarks • 13 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 )
Benchmarks
These leaderboards are used to track progress in Weakly Supervised Object Detection
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Libraries
Use these libraries to find Weakly Supervised Object Detection models and implementationsDatasets
ImageNet
MS COCO
Charades
Foggy Cityscapes
HICO-DET
PASCAL VOC 2007
PASCAL VOC 2012 test
Clipart1k
Most implemented papers
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
vadimkantorov/contextlocnet
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The additive model encourages the predicted object region to be supported by its surrounding context region.
Variational Bayesian Multiple Instance Learning With Gaussian Processes
Gaussian Processes (GPs) are effective Bayesian predictors.
Soft Proposal Networks for Weakly Supervised Object Localization
yeezhu/SPN.pytorch
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ICCV 2017
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
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.
Min-Entropy Latent Model for Weakly Supervised Object Detection
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.
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
ac-rodriguez/privilegedpooling
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CVPR 2019
We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks.
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
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.
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
makhtar17004/orientation-aware-firearm-detection
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The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors.
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation
shenyunhang/WS-JDS
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CVPR 2019
In this paper, we join weakly supervised object detection and segmentation tasks with a multi-task learning scheme for the first time, which uses their respective failure patterns to complement each other's learning.
WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection
researchmm/WSOD2
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We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations.
