The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

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 …

We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as
dense visual descriptors. We observe and empirically demonstrate that such features, when …

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable
image classifier that integrates the power of deep learning and the interpretability of case …

Interpreting and explaining the behavior of deep neural networks is critical for many tasks.
Explainable AI provides a way to address this challenge, mostly by providing per-pixel …

Humans usually explain their reasoning (eg classification) by dissecting the image and
pointing out the evidence from these parts to the concepts in their minds. Inspired by this …

Fine-grained visual categorization (FGVC) aims at recognizing objects from similar
subordinate categories, which is challenging and practical for human's accurate automatic …