Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …

Prototype-based methods use interpretable representations to address the black-box nature
of deep learning models, in contrast to post-hoc explanation methods that only approximate …

Visual attention has become a popular and widely used component for image recognition.
Although various attention-based methods have been proposed and achieved relatively …

The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image
classification, can also visualize global interpretations through attention-based contributions …

Fine-grained image recognition (FGIR) is a challenging task as it requires the recognition of
sub-categories with subtle differences. Recently, the swin transformer has shown impressive …

Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

With the development of advanced information and intelligence technologies, precision
agriculture has become an effective solution to monitor and prevent crop pests and …

In fine-grained image recognition (FGIR), the localization and amplification of region
attention is an important factor, which has been explored extensively convolutional neural …