How Quality Affects Deep Neural Networks in Fine-Grained Image Classification

arXiv:2405.05742v1 Announce Type: new
Abstract: In this paper, we propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system. Scores given by existing NRIQA methods on the same image may vary and not be as independent of natural image augmentations as expected, which weakens their connection and explainability to fine-grained image classification. Taking the three most commonly adopted image augmentation configurations -- cropping, rotating, and blurring -- as …

abstract arxiv assessment classification cs.cv fine-grained image independent natural networks neural networks paper performance quality reference strategy type