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 …

Computer vision (CV) is the process of using machines to understand and analyze imagery,
which is an integral branch of artificial intelligence. Among various research areas of CV …

Fine-grained image recognition is very challenging due to the difficulty of capturing both
semantic global features and discriminative local features. Meanwhile, these two features …

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 …

Deep convolutional neural networks (CNN) have seen tremendous success in large-scale
generic object recognition. In comparison with generic object recognition, fine-grained …

Deep learning methods have achieved remarkable success in fine-grained visual
categorization. Such successful categorization at sub-ordinate level, eg, different animal or …

Current approaches for fine-grained recognition do the following: First, recruit experts to
annotate a dataset of images, optionally also collecting more structured data in the form of …

Fine-grained recognition poses the unique challenge of capturing subtle inter-class
differences under considerable intra-class variances (eg, beaks for bird species) …

Given a food image, can a fine-grained object recognition engine tell" which restaurant
which dish" the food belongs to? Such ultra-fine grained image recognition is the key for …

Recognizing fine-grained categories (eg, bird species) is difficult due to the challenges of
discriminative region localization and fine-grained feature learning. Existing approaches …