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Weakly Supervised Object Detection

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 )

Papers

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Showing 101–125 of 142 papers

TitleStatusHype
Enabling Deep Residual Networks for Weakly Supervised Object Detection—0
Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion—0
Exploiting Web Images for Weakly Supervised Object Detection—0
Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection—0
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation—0
Few-shot Weakly-Supervised Object Detection via Directional Statistics—0
Generative Adversarial Learning Towards Fast Weakly Supervised Detection—0
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection—0
Learning Better Visual Representations for Weakly-Supervised Object Detection Using Natural Language Supervision—0
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
Learning Object Scale With Click Supervision for Object Detection—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images—0
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection—0
Negative Prototypes Guided Contrastive Learning for WSOD—0
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection—0
Object-Aware Instance Labeling for Weakly Supervised Object Detection—0
On learning to localize objects with minimal supervision—0
Online Active Proposal Set Generation for Weakly Supervised Object Detection—0
Online progressive instance-balanced sampling for weakly supervised object detection—0
Optimizing Region Selection for Weakly Supervised Object Detection—0
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation—0
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement—0
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN—0
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks—0
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