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MetaAnchor: Learning to Detect Objects with Customized Anchors

arXiv 1807.00980 ↗First posted 3 Jul 2018cs.CVcited by 21

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Abstract (arXiv)

We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks. Unlike many previous detectors model anchors via a predefined manner, in MetaAnchor anchor functions could be dynamically generated from the arbitrary customized prior boxes. Taking advantage of weight prediction, MetaAnchor is able to work with most of the anchor-based object detection systems such as RetinaNet. Compared with the predefined anchor scheme, we empirically find that MetaAnchor is more robust to anchor settings and bounding box distributions; in addition, it also shows the potential on transfer tasks. Our experiment on COCO detection task shows that MetaAnchor consistently outperforms the counterparts in various scenarios.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2018.

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Citing papers per year: 2021: 13; 2022: 4; 2023: 2; 2024: 1; 2025: 1.

21 papers.

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  1. 2021Dynamic Neural Networks: A Surveycited by 405
  2. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 177
  3. 2022AdaMixer: A Fast-Converging Query-Based Object Detectorcited by 78
  4. 2021Image Synthesis via Semantic Compositioncited by 45
  5. 2022Towards Data-Efficient Detection Transformerscited by 34
  6. 2021Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learningcited by 12
  7. 2021Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Imagescited by 8
  8. 2022Spatio-Temporal Learnable Proposals for End-to-End Video Object Detectioncited by 8
  9. 2021LLA: Loss-aware Label Assignment for Dense Pedestrian Detectioncited by 7
  10. 2021Anchor Pruning for Object Detectioncited by 5
  11. 2021Plot2Spectra: an Automatic Spectra Extraction Toolcited by 5
  12. 2021Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentationcited by 5
9 more
  1. 2021Revisiting the Loss Weight Adjustment in Object Detectioncited by 3
  2. 2022ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimationcited by 2
  3. 2023ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentationcited by 2
  4. 2021Probabilistic Ranking-Aware Ensembles for Enhanced Object Detectionscited by 1
  5. 2025Density-based Object Detection in Crowded Scenescited by 1
  6. 2021RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detectioncited by 0
  7. 2021Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detectioncited by 0
  8. 2023Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detectioncited by 0
  9. 2024PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encodingcited by 0