The existing methods of fine-grained image recognition mainly devote to learning subtle yet
discriminative features from the high-resolution input. However, their performance …

Efficient analysis of Haematoxylin and Eosin stained histopathology images has become a
challenge in digital pathology work-flow. We propose a Multi-view Attention Superpixel …

Ship classification from synthetic aperture radar (SAR) images tends to be a hotspot in the
remote sensing community. Currently, more efforts have been made to the single …

Existing fine-grained image recognition methods are difficult to learn complete discriminative
features from low-resolution (LR) data, because the original subtle inter-class distinctions …

Over the past few years, a significant progress has been made in deep convolutional neural
networks (CNNs)-based image recognition. This is mainly due to the strong ability of such …

Fine-grained visual classification (FGVC) is becoming an important research field, due to its
wide applications and the rapid development of computer vision technologies. The current …

The key to fine-grained aircraft recognition is discovering the subtle traits that can distinguish
different subcategories. Early approaches leverage part annotations of fine-grained objects …

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for
improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we …

For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of
blood cells is very important. Traditional deep CNNs lead to overfitting when it receives …

Despite achieving state-of-the-art performance, deep learning methods generally require a
large amount of labeled data during training and may suffer from overfitting when the sample …