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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
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
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm LocalizationCode0
Min-Entropy Latent Model for Weakly Supervised Object DetectionCode0
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object DetectionCode0
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object DetectionCode0
Cyclic Guidance for Weakly Supervised Joint Detection and SegmentationCode0
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom UpCode0
Weakly Supervised Deep Detection NetworksCode0
WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object DetectionCode0
Anatomy-Driven Pathology Detection on Chest X-raysCode0
LoANs: Weakly Supervised Object Detection with Localizer Assessor NetworksCode0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Self Paced Deep Learning for Weakly Supervised Object DetectionCode0
Weakly Supervised Object Detection for Automatic Tooth-marked Tongue RecognitionCode0
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification FrameworkCode0
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object DetectionCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
CaT: Weakly Supervised Object Detection with Category TransferCode0
Smart Feature is What You NeedCode0
Soft Proposal Networks for Weakly Supervised Object LocalizationCode0
Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data VolumeCode0
A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial AnnotationsCode0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical videoCode0
VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object DetectionCode0
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationCode0
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