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Weakly-Supervised Object Localization

Weakly supervised object localization (WSOL) learns to localize objects with only image-level labels, no object level labels (bonding boxes, etc.,) is needed. It is more attractive since image-level labels are much easier and cheaper to obtain.

Papers

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Showing 101–140 of 140 papers

TitleStatusHype
Hierarchical Complementary Learning for Weakly Supervised Object Localization—0
How hard can it be? Estimating the difficulty of visual search in an image—0
Improved Techniques For Weakly-Supervised Object Localization—0
Improving Few-shot Learning with Weakly-supervised Object Localization—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label—0
Information Entropy Based Feature Pooling for Convolutional Neural Networks—0
Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning—0
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization—0
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation—0
Leveraging Activations for Superpixel Explanations—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Location-free Human Pose Estimation—0
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization—0
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization—0
ML-LocNet: Improving Object Localization with Multi-view Learning Network—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization—0
Multiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization—0
Object-Extent Pooling for Weakly Supervised Single-Shot Localization—0
Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization—0
Rethinking the Localization in Weakly Supervised Object Localization—0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization—0
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
Towards Two-Stream Foveation-based Active Vision Learning—0
Training object class detectors with click supervision—0
Two-Phase Learning for Weakly Supervised Object Localization—0
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection—0
Weakly Supervised Localization Using Background Images—0
Weakly Supervised Object Localization and Detection: A Survey—0
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset—0
Weakly Supervised Object Localization Using Size Estimates—0
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation—0
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