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 )


Use these libraries to find Weakly Supervised Object Detection models and implementations

Most implemented papers

Multiple Instance Detection Network with Online Instance Classifier Refinement

ppengtang/oicr CVPR 2017

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 9 Jul 2018

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

hbilen/WSDDN CVPR 2016

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

naoto0804/cross-domain-detection CVPR 2018

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 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.

Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection

NVlabs/wetectron 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 3 Aug 2020

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 19 Nov 2022

Drawings are powerful means of pictorial abstraction and communication.

Self Paced Deep Learning for Weakly Supervised Object Detection

moinnabi/SelfPacedDeepLearning 24 May 2016

The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training.