Maximum-entropy fine grained classification
Abstract Fine-Grained Visual Classification (FGVC) is an important computer vision problem
that involves small diversity within the different classes, and often requires expert annotators …
that involves small diversity within the different classes, and often requires expert annotators …
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 …
Learning to navigate for fine-grained classification
Fine-grained classification is challenging due to the difficulty of finding discriminative
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
Embedding label structures for fine-grained feature representation
Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-
grained image classification, which aims to differentiate the subtle differences among …
grained image classification, which aims to differentiate the subtle differences among …
A cascaded part-based system for fine-grained vehicle classification
M Biglari, A Soleimani… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Vehicle make and model recognition (VMMR) has become an important part of intelligent
transportation systems. VMMR can be useful when license plate recognition is not feasible …
transportation systems. VMMR can be useful when license plate recognition is not feasible …
Attention convolutional binary neural tree for fine-grained visual categorization
Fine-grained visual categorization (FGVC) is an important but challenging task due to high
intra-class variances and low inter-class variances caused by deformation, occlusion …
intra-class variances and low inter-class variances caused by deformation, occlusion …
[HTML][HTML] Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained visual classification
Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …
Progressive co-attention network for fine-grained visual classification
Fine-grained visual classification aims to recognize images belonging to multiple sub-
categories within a same category. It is a challenging task due to the inherently subtle …
categories within a same category. It is a challenging task due to the inherently subtle …
Feature boosting, suppression, and diversification for fine-grained visual classification
J Song, R Yang - … Joint Conference on Neural Networks (IJCNN …, 2021 - ieeexplore.ieee.org
Learning feature representation from discriminative local regions plays a key role in fine-
grained visual classification. Employing attention mechanisms to extract part features has …
grained visual classification. Employing attention mechanisms to extract part features has …
Benchmark platform for ultra-fine-grained visual categorization beyond human performance
Deep learning methods have achieved remarkable success in fine-grained visual
categorization. Such successful categorization at sub-ordinate level, eg, different animal or …
categorization. Such successful categorization at sub-ordinate level, eg, different animal or …