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 …
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 …
A collaborative gated attention network for fine-grained visual classification
Q Zhu, W Kuang, Z Li - Displays, 2023 - Elsevier
Fine-grained image classification aims at subdividing large coarse-grained categories into
finer-grained subcategories. Most existing fine-grained research methods use a single …
finer-grained subcategories. Most existing fine-grained research methods use a single …
Multiresolution discriminative mixup network for fine-grained visual categorization
K Xu, R Lai, L Gu, Y Li - IEEE Transactions on Neural Networks …, 2021 - ieeexplore.ieee.org
Fine-grained visual categorization (FGVC) is a challenging task because there are many
hard examples existing between fine-grained classes which differ subtly in particular local …
hard examples existing between fine-grained classes which differ subtly in particular local …
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 …
Progressive learning of category-consistent multi-granularity features for fine-grained visual classification
Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …
classification tasks due to the inherently subtle intra-class object variations. Recent works …
Fine-grained visual classification with high-temperature refinement and background suppression
Fine-grained visual classification is a challenging task due to the high similarity between
categories and distinct differences among data within one single category. To address the …
categories and distinct differences among data within one single category. To address the …
[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 …
Fine-grained visual classification via progressive multi-granularity training of jigsaw patches
Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …
classification tasks due to the inherently subtle intra-class object variations. Recent works …
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 …