Fine-grained classification with noisy labels
Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …
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 …
Low-rank bilinear pooling for fine-grained classification
Pooling second-order local feature statistics to form a high-dimensional bilinear feature has
been shown to achieve state-of-the-art performance on a variety of fine-grained …
been shown to achieve state-of-the-art performance on a variety of fine-grained …
[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 …
See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification
Data augmentation is usually adopted to increase the amount of training data, prevent
overfitting and improve the performance of deep models. However, in practice, random data …
overfitting and improve the performance of deep models. However, in practice, random data …
Stochastic partial swap: Enhanced model generalization and interpretability for fine-grained recognition
Learning mid-level representation for fine-grained recognition is easily dominated by a
limited number of highly discriminative patterns, degrading its robustness and generalization …
limited number of highly discriminative patterns, degrading its robustness and generalization …
On the eigenvalues of global covariance pooling for fine-grained visual recognition
The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-
class variations are difficult to be captured. One notable research line uses the Global …
class variations are difficult to be captured. One notable research line uses the Global …
Elope: Fine-grained visual classification with efficient localization, pooling and embedding
H Hanselmann, H Ney - … of the IEEE/CVF winter conference …, 2020 - openaccess.thecvf.com
The task of fine-grained visual classification (FGVC) deals with classification problems that
display a small inter-class variance such as distinguishing between different bird species or …
display a small inter-class variance such as distinguishing between different bird species or …
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 …
AP-CNN: Weakly supervised attention pyramid convolutional neural network for fine-grained visual classification
Classifying the sub-categories of an object from the same super-category (eg, bird species
and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature …
and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature …