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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 76–100 of 140 papers

TitleStatusHype
DANet: Divergent Activation for Weakly Supervised Object LocalizationCode0
Min-Entropy Latent Model for Weakly Supervised Object DetectionCode0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object LocalizationCode0
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained VideosCode0
Background-aware Classification Activation Map for Weakly Supervised Object LocalizationCode0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization—0
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
Category-aware Allocation Transformer for Weakly Supervised Object Localization—0
Compression and Localization in Reinforcement Learning for ATARI Games—0
Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization—0
Deep Self-Taught Learning for Weakly Supervised Object Localization—0
Density-Based Region Search with Arbitrary Shape for Object Localization—0
Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization—0
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition—0
Dual-attention Focused Module for Weakly Supervised Object Localization—0
Entropy Guided Adversarial Model for Weakly Supervised Object Localization—0
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization—0
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning—0
Fine-Grained Attention for Weakly Supervised Object Localization—0
Foreground Activation Maps for Weakly Supervised Object Localization—0
GridMix: Strong regularization through local context mapping—0
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