mlsanityData to 14 Oct 2026

LGA-RCNN: Loss-Guided Attention for Object Detection

arXiv 2104.13763 ↗First posted 28 Apr 2021cs.CVcited by 0

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Abstract (arXiv)

Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed, which boosts the development of related researchs. However, existing detection methods still suffer from undesirable performance under challenges such as camouflage, blur, inter-class similarity, intra-class variance and complex environment. To address this issue, we propose LGA-RCNN which utilizes a loss-guided attention (LGA) module to highlight representative region of objects. Then, those highlighted local information are fused with global information for precise classification and localization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 28 Apr 2021.

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22 of 34 references.

References · 22

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  6. 2015Fast R-CNNcited by 2,933
  7. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  8. 2017Non-local Neural Networkscited by 2,293
  9. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  10. 2019Objects as Pointscited by 1,175
  11. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  12. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
10 more
  1. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  2. 2015WIDER FACE: A Face Detection Benchmarkcited by 298
  3. 2017CityPersons: A Diverse Dataset for Pedestrian Detectioncited by 172
  4. 2019Bottom-up Object Detection by Grouping Extreme and Center Pointscited by 156
  5. 2018Grid R-CNNcited by 85
  6. 2019Context R-CNN: Long Term Temporal Context for Per-Camera Object Detectioncited by 45
  7. 2019MinneApple: A Benchmark Dataset for Apple Detection and Segmentationcited by 39
  8. 2019WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wildcited by 39
  9. 2017Light-Head R-CNN: In Defense of Two-Stage Object Detectorcited by 30
  10. 2017RON: Reverse Connection with Objectness Prior Networks for Object Detectioncited by 27

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