| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 70.2 | WeakSAM-MIST-DINO (with SAM) | 2024-02-22 |
| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 69.2 | WeakSAM-MIST-Faster RCNN (with SAM) | 2024-02-22 |
| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 66.9 | WeakSAM-MIST (with SAM) | 2024-02-22 |
| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 63.7 | WeakSAM-OICR-DINO (with SAM) | 2024-02-22 |
| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 62.9 | WeakSAM-OICR-Faster RCNN (with SAM) | 2024-02-22 |
| WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition | ✓ Link | 58.4 | WeakSAM-OICR (with SAM) | 2024-02-22 |
| Object Discovery via Contrastive Learning for Weakly Supervised Object Detection | ✓ Link | 54.6 | OD-WSCL | 2022-08-16 |
| Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection | ✓ Link | 53.6 | CASD(VGG16) | 2020-10-22 |
| Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection | ✓ Link | 52.1 | wetectron(single-model) | 2020-04-09 |
| C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection | | 50.3 | C-MIDN+FRCNN | 2019-10-01 |
| Dissimilarity Coefficient based Weakly Supervised Object Detection | | 49.5 | Pred Net (Ens) | 2018-11-25 |
| Towards Precise End-to-end Weakly Supervised Object Detection Network | ✓ Link | 49.5 | Our-Ens | 2019-11-27 |
| Object-Aware Instance Labeling for Weakly Supervised Object Detection | | 48.1 | Ours + FRCNN | 2019-08-10 |
| Utilizing the Instability in Weakly Supervised Object Detection | | 48.0 | Ours+FRCNN | 2019-06-14 |
| W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection | | 47.8 | WSD+PGE+PGA+FSD2 | 2018-06-01 |
| WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection | | 47.2 | WSOD2 | 2019-09-11 |
| C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | ✓ Link | 46.7 | C-MIL | 2019-04-11 |
| Object Instance Mining for Weakly Supervised Object Detection | ✓ Link | 46.4 | OIM+IR+FRCNN | 2020-02-04 |
| Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation | ✓ Link | 46.1 | WS-JDS FRCNN | 2019-06-01 |
| PCL: Proposal Cluster Learning for Weakly Supervised Object Detection | ✓ Link | 44.2 | PCL-OB-G-Ens + FRCNN | 2018-07-09 |
| You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection | | 43.2 | OICR + W-RPN | 2019-06-01 |
| Zigzag Learning for Weakly Supervised Object Detection | | 42.9 | ZLDN-L | 2018-04-25 |
| Exploiting Web Images for Weakly Supervised Object Detection | | 42.8 | WebRelETH | 2017-07-27 |
| Multiple Instance Detection Network with Online Instance Classifier Refinement | ✓ Link | 42.5 | OICR-Ens + FRCNN | 2017-04-01 |
| Min-Entropy Latent Model for Weakly Supervised Object Detection | ✓ Link | 42.4 | MELM | 2019-02-16 |
| Deep Self-Taught Learning for Weakly Supervised Object Localization | | 38.3 | Deep Self-Taught Learning | 2017-04-18 |
| Weakly Supervised Cascaded Convolutional Networks | | 37.9 | WCCN | 2016-11-24 |
| Variational Bayesian Multiple Instance Learning With Gaussian Processes | ✓ Link | 37.8 | LM-VGPMIL | 2017-07-01 |
| Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images | | 36.6 | NSOD | 2019-12-01 |
| Few-Example Object Detection with Model Communication | ✓ Link | 35.4 | MSLPD | 2017-06-26 |
| ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization | ✓ Link | 35.3 | WSDDN + context | 2016-09-14 |
| Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization | | 35.2 | Our scheme | 2019-10-04 |