4 papers
An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation
Zheming Zuo, Joseph Smith, Jonathan Stonehouse +1
Image segmentation is a crucial task in computer vision, with wide-ranging applications in industry. The Segment Anything Model (SAM) has recently attracted intensive attention; ho…
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework
Zheming Zuo, Joseph Smith, Jonathan Stonehouse +1
In the realm of practical fine-grained visual classification applications rooted in deep learning, a common scenario involves training a model using a pre-existing dataset. Subsequ…
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification
Joseph Smith, Zheming Zuo, Jonathan Stonehouse +1
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…
Camera Bias in a Fine Grained Classification Task
Philip T. Jackson, Stephen Bonner, Ning Jia +3
We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a mode…