mlsanityData to 14 Oct 2026

Rethinking Features-Fused-Pyramid-Neck for Object Detection

arXiv 2505.12820 ↗First posted 19 May 2025cs.CVcited by 5

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Abstract (arXiv)

Multi-head detectors typically employ a features-fused-pyramid-neck for multi-scale detection and are widely adopted in the industry. However, this approach faces feature misalignment when representations from different hierarchical levels of the feature pyramid are forcibly fused point-to-point. To address this issue, we designed an independent hierarchy pyramid (IHP) architecture to evaluate the effectiveness of the features-unfused-pyramid-neck for multi-head detectors. Subsequently, we introduced soft nearest neighbor interpolation (SNI) with a weight downscaling factor to mitigate the impact of feature fusion at different hierarchies while preserving key textures. Furthermore, we present a features adaptive selection method for down sampling in extended spatial windows (ESD) to retain spatial features and enhance lightweight convolutional techniques (GSConvE). These advancements culminate in our secondary features alignment solution (SA) for real-time detection, achieving state-of-the-art results on Pascal VOC and MS COCO. Code will be released at https://github.com/AlanLi1997/rethinking-fpn. This paper has been accepted by ECCV2024 and published on Springer Nature.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 40 references that are in the map. First posted on arXiv 19 May 2025.

Builds on

35 of 40 references.

References · 35

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  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
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  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  11. 2015Fast R-CNNcited by 2,933
  12. 2018YOLOv3: An Incremental Improvementcited by 2,659
23 more
  1. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  2. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  5. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
  6. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  7. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  8. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  9. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  10. 2018Path Aggregation Network for Instance Segmentationcited by 798
  11. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  12. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 488
  13. 2019GhostNet: More Features from Cheap Operationscited by 410
  14. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  15. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  16. 2019Scale-Aware Trident Networks for Object Detectioncited by 159
  17. 2017DSSD : Deconvolutional Single Shot Detectorcited by 131
  18. 2017An Analysis of Scale Invariance in Object Detection - SNIPcited by 120
  19. 2019Rethinking Classification and Localization for Object Detectioncited by 98
  20. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  21. 2019Learning Spatial Fusion for Single-Shot Object Detectioncited by 59
  22. 2017FSSD: Feature Fusion Single Shot Multibox Detectorcited by 43
  23. 2022Slim-neck by GSConv: A lightweight-design for real-time detector architecturescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025A biologically inspired separable learning vision model for real-time traffic object perception in Darkcited by 0
  2. 2025PT-DETR: Small Target Detection Based on Partially-Aware Detail Focuscited by 0
  3. 2026FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detectioncited by 0
  4. 2026Context-Guided Semantic Alignment for Feature Fusion Networkscited by 0
  5. 2026From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detectioncited by 0