Intra-class part swapping for fine-grained image classification
Recent works such as Mixup and Cutmix have demonstrated the effectiveness of
augmenting training data for deep models. These methods generate new data by generally …
augmenting training data for deep models. These methods generate new data by generally …
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 …
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 …
Weakly supervised complementary parts models for fine-grained image classification from the bottom up
Given a training dataset composed of images and corresponding category labels, deep
convolutional neural networks show a strong ability in mining discriminative parts for image …
convolutional neural networks show a strong ability in mining discriminative parts for image …
Focus longer to see better: Recursively refined attention for fine-grained image classification
Abstract Deep Neural Network has shown great strides in the coarse-grained image
classification task. It was in part due to its strong ability to extract discriminative feature …
classification task. It was in part due to its strong ability to extract discriminative feature …
Beyond the attention: Distinguish the discriminative and confusable features for fine-grained image classification
Learning subtle discriminative features plays a significant role in fine-grained image
classification. Existing methods usually extract the distinguishable parts through the …
classification. Existing methods usually extract the distinguishable parts through the …
Snapmix: Semantically proportional mixing for augmenting fine-grained data
Data mixing augmentation has proved effective in training deep models. Recent methods
mix labels mainly according to the mixture proportion of image pixels. Due to the major …
mix labels mainly according to the mixture proportion of image pixels. Due to the major …
Group based deep shared feature learning for fine-grained image classification
Fine-grained image classification has emerged as a significant challenge because objects
in such images have small inter-class visual differences but with large variations in pose …
in such images have small inter-class visual differences but with large variations in pose …
Learning mutually exclusive part representations for fine-grained image classification
Fine-grained image classification (FGIC) aims to separate different subcategories from one
general superclass, which requires the classification model to extract distinctive …
general superclass, which requires the classification model to extract distinctive …
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 …