mlsanityData to 14 Oct 2026

Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification

arXiv 2205.01920 ↗First posted 4 May 2022cs.CVcited by 0

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Abstract (arXiv)

Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied. This paper firstly finds that fine-grained data, class imbalance and various shooting conditions preclude the representational ability of general image classification. Moreover, the MAVOC dataset has scene aggregation characteristics. By exploiting these properties, we propose Scene Clustering Based Pseudo-labeling Strategy (SCP-Label), a simple yet effective method to employ in post-processing. The SCP-Label brings greater accuracy by assigning the same label to objects within the same scene while also mitigating bias and confusion with model ensembles. Its performance surpasses the official baseline by a large margin of +20.57% Accuracy on Track 1 (SAR), and +31.86% Accuracy on Track 2 (SAR+EO), demonstrating the potential of SCP-Label as post-processing. Finally, we win the championship both on Track1 and Track2 in the CVPR 2022 Perception Beyond the Visible Spectrum (PBVS) Workshop MAVOC Challenge. Our code is available at https://github.com/HowieChangchn/SCP-Label.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 4 May 2022.

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17 of 42 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  5. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  7. 2019Searching for MobileNetV3cited by 1,994
  8. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  9. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 989
  10. 2017Focal Loss for Dense Object Detectioncited by 752
  11. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
  12. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
5 more
  1. 2018What is the Effect of Importance Weighting in Deep Learning?cited by 251
  2. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 227
  3. 2018xView: Objects in Context in Overhead Imagerycited by 146
  4. 2017SAR Image Despeckling Using a Convolutional Neural Networkcited by 24
  5. 2018Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNNcited by 18

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