Object-part attention model for fine-grained image classification
Fine-grained image classification is to recognize hundreds of subcategories belonging to
the same basic-level category, such as 200 subcategories belonging to the bird, which is …
the same basic-level category, such as 200 subcategories belonging to the bird, which is …
Weakly supervised learning of part selection model with spatial constraints for fine-grained image classification
Fine-grained image classification is challenging due to the large intra-class variance and
small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to …
small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to …
Learning semantically enhanced feature for fine-grained image classification
We aim to provide a computationally cheap yet effective approach for fine-grained image
classification (FGIC) in this letter. Unlike previous methods that rely on complex part …
classification (FGIC) in this letter. Unlike previous methods that rely on complex part …
TOAN: Target-oriented alignment network for fine-grained image categorization with few labeled samples
In this paper, we study the fine-grained categorization problem under the few-shot setting, ie,
each fine-grained class only contains a few labeled examples, termed Fine-Grained Few …
each fine-grained class only contains a few labeled examples, termed Fine-Grained Few …
Cross-part learning for fine-grained image classification
Recent techniques have achieved remarkable improvements depended on mining subtle
yet distinctive features for fine-grained visual classification (FGVC). While prior works directly …
yet distinctive features for fine-grained visual classification (FGVC). While prior works directly …
Fine-grained recognition with learnable semantic data augmentation
Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …
differentiating objects belonging to multiple subordinate categories within the same meta …
Re-rank coarse classification with local region enhanced features for fine-grained image recognition
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 …
semantic global features and discriminative local features. Meanwhile, these two features …
Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification
X Ke, Y Cai, B Chen, H Liu, W Guo - Pattern Recognition, 2023 - Elsevier
Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-
categories of the same super-category. The key to solving fine-grained classification …
categories of the same super-category. The key to solving fine-grained classification …
Filtration and distillation: Enhancing region attention for fine-grained visual categorization
Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual
Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly …
Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly …
Diversified visual attention networks for fine-grained object classification
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 …
However, it is a quite challenging problem due to the subtle interclass difference and large …