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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 51–60 of 140 papers

TitleStatusHype
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization—0
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object LocalizationCode1
Localizing Objects with Self-Supervised Transformers and no LabelsCode1
F-CAM: Full Resolution Class Activation Maps via Guided Parametric UpscalingCode1
Causal Explanation of Convolutional Neural NetworksCode0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
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