Research map / Detection and segmentation / CNN features
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add multi-scale evaluation to single-scale inference (ablation) | Associative Embedding · MS-COCO test-dev | AP: 0.566 -> 0.630 | Helped |
| Add single-person pose refinement to single-scale inference (ablation) | Associative Embedding · MS-COCO test-dev | AP: 0.566 -> 0.628 | Helped |
| Add single-person pose refinement to multi-scale inference (ablation) | Associative Embedding · MS-COCO test-dev | AP: 0.630 -> 0.655 | Helped |
| Replace predicted detections with ground-truth detections while keeping predicted tags (ablation) | Associative Embedding · MS-COCO held-out 500 training images | AP: 59.2 -> 94.0 | Helped |
| Use associative embedding for instance segmentation | Stacked hourglass · PASCAL VOC 2012 val, IOU=0.5 and IOU=0.7 | mAP: 35.1 at IOU=0.5; 26.0 at IOU=0.7, compared with MNC 63.5 and 41.5 | Mixed |
Closest papers by meaning
- Multi-Person Articulated Tracking With Spatial and Temporal Embeddings (CVPR 2019 · similarity 0.87)
- Group Collaborative Learning for Co-Salient Object Detection (CVPR 2021 · similarity 0.86)
- A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation (ICML 2016 · similarity 0.86)
- Group R-CNN for Weakly Semi-Supervised Object Detection With Points (CVPR 2022 · similarity 0.85)
- Single-Image Piece-Wise Planar 3D Reconstruction via Associative Embedding (CVPR 2019 · similarity 0.85)
- Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval (CVPR 2024 · similarity 0.84)
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