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Scale-Aware Trident Networks for Object Detection

arXiv 1901.01892 ↗First posted 7 Jan 2019cs.CVcited by 159

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Abstract (arXiv)

Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2019.

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Citing papers per year: 2021: 52; 2022: 33; 2023: 28; 2024: 17; 2025: 17; 2026: 12.

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  1. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  2. 2021Conditional DETR for Fast Training Convergencecited by 422
  3. 2021GMFlow: Learning Optical Flow via Global Matchingcited by 324
  4. 2022Unifying Flow, Stereo and Depth Estimationcited by 270
  5. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  6. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  7. 2021Probabilistic two-stage detectioncited by 130
  8. 2022UIU-Net: U-Net in U-Net for Infrared Small Object Detectioncited by 124
  9. 2022Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalizationcited by 122
  10. 2022Towards Large-Scale Small Object Detection: Survey and Benchmarkscited by 119
  11. 2021You Only Look One-level Featurecited by 116
  12. 2022AdaMixer: A Fast-Converging Query-Based Object Detectorcited by 78
38 more
  1. 2021QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detectioncited by 70
  2. 2022Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmarkcited by 56
  3. 2023PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Cloudscited by 54
  4. 2023Multi-Modal Classifiers for Open-Vocabulary Object Detectioncited by 53
  5. 2021A Normalized Gaussian Wasserstein Distance for Tiny Object Detectioncited by 52
  6. 2022RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detectioncited by 51
  7. 2021ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenescited by 47
  8. 2021Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagerycited by 44
  9. 2021Parsing Table Structures in the Wildcited by 43
  10. 2021Label, Verify, Correct: A Simple Few Shot Object Detection Methodcited by 43
  11. 2023Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detectioncited by 43
  12. 2021Injecting Semantic Concepts into End-to-End Image Captioningcited by 41
  13. 2023StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-trainingcited by 39
  14. 2021Disentangle Your Dense Object Detectorcited by 37
  15. 2024Frequency-Adaptive Dilated Convolution for Semantic Segmentationcited by 34
  16. 2021A Review on Deep Learning in UAV Remote Sensingcited by 32
  17. 2022Dense Learning based Semi-Supervised Object Detectioncited by 32
  18. 2023WEDGE: A multi-weather autonomous driving dataset built from generative vision-language modelscited by 28
  19. 2021GraphFPN: Graph Feature Pyramid Network for Object Detectioncited by 25
  20. 2021Scale-aware Automatic Augmentation for Object Detectioncited by 23
  21. 2021Rank & Sort Loss for Object Detection and Instance Segmentationcited by 22
  22. 2022Localization Distillation for Object Detectioncited by 19
  23. 2021FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRIcited by 18
  24. 2023Cascaded Zoom-in Detector for High Resolution Aerial Imagescited by 17
  25. 2022HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perceptioncited by 16
  26. 2021On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classificationcited by 15
  27. 2022YOLO-FaceV2: A Scale and Occlusion Aware Face Detectorcited by 15
  28. 2023Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Artcited by 15
  29. 2021From SLAM to Situational Awareness: Challenges and Surveycited by 14
  30. 2022Prediction-Guided Distillation for Dense Object Detectioncited by 14
  31. 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
  32. 2023MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detectioncited by 13
  33. 2021Reconcile Prediction Consistency for Balanced Object Detectioncited by 11
  34. 2021Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Imagescited by 11
  35. 2021Mutual Supervision for Dense Object Detectioncited by 10
  36. 2022Continuous Human Action Recognition for Human-Machine Interaction: A Reviewcited by 10
  37. 2022Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detectioncited by 10
  38. 2022Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectorscited by 10

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