We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …

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

Recognizing fine-grained categories (eg, bird species) highly relies on discriminative part
localization and part-based fine-grained feature learning. Existing approaches …

Fine-grained recognition is one of the most difficult topics in visual recognition, which aims at
distinguishing confusing categories such as bird species within a genus. The information of …

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 …

Discriminative region localization and feature learning are crucial for fine-grained visual
recognition. Existing approaches solve this issue by attention mechanism or part based …

Fine-grained image recognition is a challenging computer vision problem, due to the small
inter-class variations caused by highly similar subordinate categories, and the large intra …

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 …

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 …

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …