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

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of
organization, from molecular to the biome. However, biological networks are noisy due to …

Semantic part localization can facilitate fine-grained categorization by explicitly isolating
subtle appearance differences associated with specific object parts. Methods for pose …

Fine-grained classification is challenging because categories can only be discriminated by
subtle and local differences. Variances in the pose, scale or rotation usually make the …

In the context of fine-grained visual categorization, the ability to interpret models as human-
understandable visual manuals is sometimes as important as achieving high classification …

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the
computer vision community will need to face in order to realize its goal of recognizing all …

Fine-grained object classification attracts increasing attention in multimedia applications.
However, it is a quite challenging problem due to the subtle interclass difference and large …

We propose an architecture for fine-grained visual categorization that approaches expert
human performance in the classification of bird species. Our architecture first computes an …

Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging
due to the highly localized and subtle differences in some specific parts. Most previous …