The Fine-Grained Image Classification task focuses on differentiating between hard-to-distinguish object classes, such as species of birds, flowers, or animals; and identifying the makes or models of vehicles.
( Image credit: Looking for the Devil in the Details )
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Chinese text recognition is more challenging than Latin text due to the large amount of fine-grained Chinese characters and the great imbalance over classes, which causes a serious overfitting problem.
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly.
#4 best model for Fine-Grained Image Classification on FGVC Aircraft
The term fine-grained visual classification (FGVC) refers to classification tasks where the classes are very similar and the classification model needs to be able to find subtle differences to make the correct prediction.
#2 best model for Fine-Grained Image Classification on FGVC Aircraft
In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples.
#2 best model for Fine-Grained Image Classification on CUB-200-2011
Given that images from distinct classes in fine-grained classification share significant features of interest, we present a new deep network architecture that explicitly models shared features and removes their effect to achieve enhanced classification results.
These distinct gate vectors inherit mutual context on semantic differences, which allow API-Net to attentively capture contrastive clues by pairwise interaction between two images.
For a single image, a self-channel interaction (SCI) module is proposed to explore channel-wise correlation within the image.
#7 best model for Fine-Grained Image Classification on FGVC Aircraft
The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them.
#8 best model for Fine-Grained Image Classification on FGVC Aircraft
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.