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Better low-resolution image classification using attributes

Better low-resolution image classification using attributes


Enhancing Fine-Grained Classification for Low Resolution Images

... proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from 32x32 to 224x224. Different experiments demonstrate the efficacy of the proposed attribute-assisted loss for low resolution fine-grained classification.


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