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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 101–142 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
Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection—0
Read, look and detect: Bounding box annotation from image-caption pairs—0
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection—0
Salvage of Supervision in Weakly Supervised Object Detection—0
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection—0
Self-supervised object detection from audio-visual correspondence—0
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection—0
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection—0
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction—0
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection—0
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection—0
Towards Object Detection from Motion—0
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information—0
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection—0
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images—0
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