Weakly Supervised Object Detection
47 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
COCO
Charades
PASCAL VOC
Foggy Cityscapes
HICO-DET
PASCAL VOC 2007
MSCOCO
Clipart1k
Most implemented papers
Multiple Instance Detection Network with Online Instance Classifier Refinement
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.
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
ppengtang/oicr
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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.
Weakly Supervised Deep Detection Networks
Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Can we detect common objects in a variety of image domains without instance-level annotations?
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
durandtibo/wildcat.pytorch
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CVPR 2017
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 Object Detection in Artworks
nicaogr/Mi_max
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ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018
We propose a method for the weakly supervised detection of objects in paintings.
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
NVlabs/wetectron
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CVPR 2020
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
nicaogr/Mi_max
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Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
barisbatuhan/dass_det_inference
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Drawings are powerful means of pictorial abstraction and communication.
Self Paced Deep Learning for Weakly Supervised Object Detection
The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training.
