Weakly-Supervised Object Localization

43 papers with code • 4 benchmarks • 2 datasets

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Greatest papers with code

Learning Deep Features for Discriminative Localization

tensorpack/tensorpack CVPR 2016

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.

Weakly-Supervised Object Localization

Eigen-CAM: Class Activation Map using Principal Components

jacobgil/pytorch-grad-cam 1 Aug 2020

At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data.

Weakly-Supervised Object Localization

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

frgfm/torch-cam IEEE 2021

To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.

Semantic Segmentation Weakly-Supervised Object Localization

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

clovaai/wsolevaluation 8 Jul 2020

In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

Few-Shot Learning Model Selection +1

Evaluating Weakly Supervised Object Localization Methods Right

clovaai/wsolevaluation CVPR 2020

In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

Few-Shot Learning Model Selection +1

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.

General Classification Image Classification +3

Soft Proposal Networks for Weakly Supervised Object Localization

yeezhu/SPN.pytorch ICCV 2017

Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.

Weakly Supervised Object Detection Weakly-Supervised Object Localization

Attention-based Dropout Layer for Weakly Supervised Object Localization

junsukchoe/ADL CVPR 2019

Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations.

Weakly-Supervised Object Localization

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

nyukat/GMIC 13 Feb 2020

In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.

Breast Cancer Detection Lesion Segmentation +2