Dual cross-attention learning for fine-grained visual categorization and object re-identification
Recently, self-attention mechanisms have shown impressive performance in various NLP
and CV tasks, which can help capture sequential characteristics and derive global …
and CV tasks, which can help capture sequential characteristics and derive global …
Sim-trans: Structure information modeling transformer for fine-grained visual categorization
Fine-grained visual categorization (FGVC) aims at recognizing objects from similar
subordinate categories, which is challenging and practical for human's accurate automatic …
subordinate categories, which is challenging and practical for human's accurate automatic …
Accurate fine-grained object recognition with structure-driven relation graph networks
Fine-grained object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …
identify the subtle distinctions between visually similar objects. However, less effort has …
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 …
Where to focus: Investigating hierarchical attention relationship for fine-grained visual classification
Object categories are often grouped into a multi-granularity taxonomic hierarchy. Classifying
objects at coarser-grained hierarchy requires global and common characteristics, while finer …
objects at coarser-grained hierarchy requires global and common characteristics, while finer …
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 …
Convolutional fine-grained classification with self-supervised target relation regularization
Fine-grained visual classification can be addressed by deep representation learning under
supervision of manually pre-defined targets (eg, one-hot or the Hadamard codes). Such …
supervision of manually pre-defined targets (eg, one-hot or the Hadamard codes). Such …
The image data and backbone in weakly supervised fine-grained visual categorization: A revisit and further thinking
Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass
classification within the same large class using only label information. Compared to general …
classification within the same large class using only label information. Compared to general …
Fine-grained visual classification using self assessment classifier
Extracting discriminative features plays a crucial role in the fine-grained visual classification
task. Most of the existing methods focus on developing attention or augmentation …
task. Most of the existing methods focus on developing attention or augmentation …
A vision transformer for fine-grained classification by reducing noise and enhancing discriminative information
Abstract Recently, several Vision Transformer (ViT) based methods have been proposed for
Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing …
Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing …