Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

Transferring the knowledge learned from large scale datasets (eg, ImageNet) via fine-tuning
offers an effective solution for domain-specific fine-grained visual categorization (FGVC) …

Attention-based learning for fine-grained image recognition remains a challenging task,
where most of the existing methods treat each object part in isolation, while neglecting the …

Computer vision builds a connection between image processing and industrials, bringing
modern perception to the automated manufacture of magnetic tiles. In this article, we …

Abstract Fine-Grained Visual Classification (FGVC) is an important computer vision problem
that involves small diversity within the different classes, and often requires expert annotators …

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 …

Abstract Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes,
along with significant intra-class variation and inter-class similarity. While prior work has …

We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …

Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent
methods have shown that the matrix power normalization can stabilize the second-order …

In this paper we report on the context and evaluation of a system for an automatic
interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as …