Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition

CVPR 2018 Yaming WangVlad I. MorariuLarry S. Davis

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Fine-Grained Image Classification CUB-200-2011 DFB Accuracy 87.4% # 21
Fine-Grained Image Classification CUB-200-2011 DFL-CNN Accuracy 87.4 # 4
Fine-Grained Image Classification FGVC Aircraft DFB-CNN Accuracy 92.0% # 17
Fine-Grained Image Classification Stanford Cars DFL-CNN Accuracy 93.8% # 15

Methods used in the Paper


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