mlsanityData to 7 Oct 2026

AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection

arXiv 2105.09596 ↗First posted 20 May 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

Recently, the anchor-free object detection model has shown great potential for accuracy and speed to exceed anchor-based object detection. Therefore, two issues are mainly studied in this article: (1) How to let the backbone network in the anchor-free object detection model learn feature extraction? (2) How to make better use of the feature pyramid network? In order to solve the above problems, Experiments show that our model has a certain improvement in accuracy compared with the current popular detection models on the COCO dataset, the designed attention mechanism module can capture contextual information well, improve detection accuracy, and use sepc network to help balance abstract and detailed information, and reduce the problem of semantic gap in the feature pyramid network. Whether it is anchor-based network model YOLOv3, Faster RCNN, or anchor-free network model Foveabox, FSAF, FCOS. Our optimal model can get 39.5% COCO AP under the background of ResNet50.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 30 references that are in the map. First posted on arXiv 20 May 2021.

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26 of 30 references.

References · 26

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  5. 2015Fast R-CNNcited by 2,930
  6. 2018YOLOv3: An Incremental Improvementcited by 2,652
  7. 2017Non-local Neural Networkscited by 2,290
  8. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  10. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  11. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  12. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
14 more
  1. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  2. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  3. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  4. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  5. 2016UnitBox: An Advanced Object Detection Networkcited by 205
  6. 2019Scale-Aware Trident Networks for Object Detectioncited by 159
  7. 2019Bottom-up Object Detection by Grouping Extreme and Center Pointscited by 156
  8. 2019Feature Selective Anchor-Free Module for Single-Shot Object Detectioncited by 139
  9. 2019Region Proposal by Guided Anchoringcited by 87
  10. 2015DenseBox: Unifying Landmark Localization with End to End Object Detectioncited by 58
  11. 2017DSOD: Learning Deeply Supervised Object Detectors from Scratchcited by 41
  12. 2019FoveaBox: Beyond Anchor-based Object Detectorcited by 37
  13. 2020Scale-Equalizing Pyramid Convolution for Object Detectioncited by 25
  14. 2019CornerNet-Lite: Efficient Keypoint Based Object Detectioncited by 24

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