A central goal of animal ecology is to observe species in the natural world. The cost and
challenge of data collection often limit the breadth and scope of ecological study. Ecologists …

The deep learning technology has shown impressive performance in various vision tasks
such as image classification, object detection and semantic segmentation. In particular …

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 …

The click feature of an image, defined as the user click frequency vector of the image on a
predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained …

When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …

Fine-grained classification is a challenging problem, due to subtle differences among highly-
confused categories. Most approaches address this difficulty by learning discriminative …

Fine-grained classification is challenging due to the difficulty of finding discriminative
features. Finding those subtle traits that fully characterize the object is not straightforward. To …

The key to solving fine-grained image categorization is finding discriminate and local
regions that correspond to subtle visual traits. Great strides have been made, with complex …

Learning subtle yet discriminative features (eg, beak and eyes for a bird) plays a significant
role in fine-grained image recognition. Existing attention-based approaches localize and …