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Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf
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🔗 Source: openaccess.thecvf.com
📊 Size: 1.63 MB
👤 Author: Yabin Zhang,Tang Hui,Kui Jia
⬇️ Downloads: 262

Summary

Fine-grained visual categorization (FGVC) is challenging due to the difficulty in acquiring enough training samples. Existing methods often adopt a strategy of pre-training models using auxiliary data, followed by fine-tuning on the target FGVC task. However, the pre-training objective does not consider the target task, resulting in suboptimal models. A new deep FGVC model, MetaFGNet, is proposed, which uses a novel regularized meta-learning objective to guide the learning of network parameters for adapting to the target FGVC task. MetaFGNet also admits a simple yet effective scheme for selecting more useful samples from auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of the proposed method.

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Fine-grained visual categorization (FGVC) is challenging due to the difficulty in acquiring enough training samples.

Technical Information

  • File Format: PDF
  • File Size: 1.63 MB
  • Pages: 16
  • Language: EN
  • Author: Yabin Zhang,Tang Hui,Kui Jia
  • Total Downloads: 262
  • Last Updated: 3 weeks ago

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