Weakly Supervised Object Detection
51 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
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection
researchmm/WSOD2
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ICCV 2019
We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations.
Towards Precise End-to-end Weakly Supervised Object Detection Network
ppengtang/pcl.pytorch
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ICCV 2019
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.
Object Instance Mining for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years.
Harmonizing Transferability and Discriminability for Adapting Object Detectors
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.
Distilling Knowledge from Refinement in Multiple Instance Detection Networks
Then, we present an adaptive supervision aggregation function that dynamically changes the aggregation criteria for selecting boxes related to one of the ground-truth classes, background, or even ignored during the generation of each refinement module supervision.
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
mikuhatsune/wsod_transfer
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ECCV 2020
In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dataset, whose categories may not overlap with the target domain.
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
DeLightCMU/CASD
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NeurIPS 2020
Moreover, the image-level category labels do not enforce consistent object detection across different transformations of the same images.
Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment
basiclab/DA-OD-MEAA-PyTorch
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Domain adaptation aims to transfer knowledge from the sourcedata with annotations to scarcely-labeled data in the target domain, which has attracted a lot of attention in recent years and facilitatedmany multimedia applications.
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection
shenyunhang/UWSOD
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NeurIPS 2020
In this paper, we propose a unified WSOD framework, termed UWSOD, to develop a high-capacity general detection model with only image-level labels, which is self-contained and does not require external modules or additional supervision.
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
DongSky/lbba_boosted_wsod
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ICCV 2021
In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset.
