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[HTML][HTML] Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification

D Liu, L Zhao, Y Wang, J Kato - Pattern Recognition, 2023 - Elsevier
Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …
被引用次数：24 相关文章

Learn from each other to Classify better:: Cross-layer mutual attention learning for fine-grained visual classification

D Liu, L Zhao, Y Wang, J Kato - 2023 - dl.acm.org
Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …

[引用][C] Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained visual classification

D Liu, L Zhao, Y Wang, J Kato - Pattern Recognition, 2023 - cir.nii.ac.jp
Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained
visual classification | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ …

Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained visual classification

D Liu, L Zhao, Y Wang, J Kato - Pattern Recognition, 2023 - ui.adsabs.harvard.edu
Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …

[引用][C] Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained visual classification

W YU - Pattern Recognition, 2023 - cir.nii.ac.jp
Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained
visual classification | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細 …