Generalized zero-shot learning aims to classify samples of seen and unseen classes by
providing only the labels of seen classes. Most GZSL methods directly associate seen …

Fine-grained visual classification (FGVC) is a critical task in the field of computer vision.
However, FGVC is full of challenges due to the large intra-class variation and small inter …

Improving the explainability of Computer Vision models based on Deep Learning has
recently become a compelling problem, ensuring reliable predictions to the end-user and …

Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …

In this paper, we propose a novel data augmentation technique employing multivariate
Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) …

Automation in structural health monitoring involves a critical step of automatic classification
of concrete defect images/videos. Although interdisciplinary research community in AI has …

Despite the great success of the existing work in fine-grained visual categorization (FGVC),
there are still several unsolved challenges, eg, poor interpretation and vagueness …

Deep metric learning has been widely used in many visual tasks. Its key idea is to increase
the similarity of positive samples and decrease the similarity of negative samples through …

Abstract Fine-Grained Visual Categorization (FGVC) aims at distinguishing subordinate-
level categories with subtle interclass differences. Although previous research shows the …

In fine-grained visual classification (FGVC), small inter-class variations and large intra-class
variations are always inherent attributes, so it is much more challenging than traditional …