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 …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

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 object recognition aims to learn effective features that can identify the subtle
differences between visually similar objects. Most of the existing works tend to amplify …

Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention
recently. Particularly given the reliance on emerging deep learning methods, which have …

The goal of self-supervised visual representation learning is to learn strong, transferable
image representations, with the majority of research focusing on object or scene level. On …

Learning with images and their classification, segmentation, localization, annotation, and
abnormally detection is one of the current challenging and exciting task for the researchers …

Deep features have been proven powerful in building accurate dense semantic
correspondences in various previous works. However, the multi-scale and pyramidal …

Attention is a mechanism that has been instrumental in driving remarkable performance
gains of deep neural network models in a host of visual, NLP and multimodal tasks. One …