mlsanityData to 14 Oct 2026

A Competitive Method to VIPriors Object Detection Challenge

arXiv 2104.09059 ↗First posted 19 Apr 2021cs.CV cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

In this report, we introduce the technical details of our submission to the VIPriors object detection challenge. Our solution is based on mmdetction of a strong baseline open-source detection toolbox. Firstly, we introduce an effective data augmentation method to address the lack of data problem, which contains bbox-jitter, grid-mask, and mix-up. Secondly, we present a robust region of interest (ROI) extraction method to learn more significant ROI features via embedding global context features. Thirdly, we propose a multi-model integration strategy to refinement the prediction box, which weighted boxes fusion (WBF). Experimental results demonstrate that our approach can significantly improve the average precision (AP) of object detection on the subset of the COCO2017 dataset.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 15 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

13 of 15 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  12. 2020GridMask Data Augmentationcited by 123
1 more
  1. 2019Weighted boxes fusion: Ensembling boxes from different object detection modelscited by 20

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Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 1.

5 papers.

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  2. 2023PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidancecited by 0
  3. 2023Optimizing rgb-d semantic segmentation through multi-modal interaction and pooling attentioncited by 0
  4. 2024AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Networkcited by 0
  5. 2025Data-Efficient Challenges in Visual Inductive Priors: A Retrospectivecited by 0