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Weakly Supervised Object Detection
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Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.
<span style="color:grey; opacity: 0.6">( Image credit: Soft Proposal Networks for Weakly Supervised Object Localization )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection
Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion
Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
Smart Feature is What You Need
Negative Prototypes Guided Contrastive Learning for WSOD
Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Weakly Supervised Open-Vocabulary Object Detection
LEOD: Label-Efficient Object Detection for Event Cameras
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
Text-image Alignment for Diffusion-based Perception
ALWOD: Active Learning for Weakly-Supervised Object Detection
Gall Bladder Cancer Detection from US Images with Only Image Level Labels
Anatomy-Driven Pathology Detection on Chest X-rays
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection
Read, look and detect: Bounding box annotation from image-caption pairs
Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information
DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth
VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation
Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Active Learning Strategies for Weakly-supervised Object Detection
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
SESS: Saliency Enhancing with Scaling and Sliding
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