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 …

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 …

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 …

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 …

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

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 …

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep
approaches for fine-grained recognition essentially enhance the mid-level learning …

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

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