Boosting few-shot fine-grained recognition with background suppression and foreground alignment
Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …
with the help of limited available samples. Undoubtedly, this task inherits the main …
Class-aware patch embedding adaptation for few-shot image classification
Abstract" A picture is worth a thousand words", significantly beyond mere a categorization.
Accompanied by that, many patches of the image could have completely irrelevant …
Accompanied by that, many patches of the image could have completely irrelevant …
Dual-path rare content enhancement network for image and text matching
Y Wang, Y Su, W Li, J Xiao, X Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Image and text matching plays a crucial role in bridging the cross-modal gap between vision
and language, and has achieved great progress due to the deep learning. However, the …
and language, and has achieved great progress due to the deep learning. However, the …
Autonomous perception and adaptive standardization for few-shot learning
Identifying unseen classes with limited labeled data for reference is a challenging task,
which is also known as few-shot learning. Generally, a knowledge-rich model is more robust …
which is also known as few-shot learning. Generally, a knowledge-rich model is more robust …
Few-shot learning meets transformer: Unified query-support transformers for few-shot classification
The goal of Few-shot classification (FSL) is to identify unseen classes with very limited
samples has attracted more and more attention. Usually, it is formulated as a metric learning …
samples has attracted more and more attention. Usually, it is formulated as a metric learning …
Counterfactual generation framework for few-shot learning
Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …
MFNet: Multiclass few-shot segmentation network with pixel-wise metric learning
In visual recognition tasks, few-shot learning requires the ability to learn object categories
with few support examples. Its re-popularity in light of the deep learning development is …
with few support examples. Its re-popularity in light of the deep learning development is …
Improving the generalization of MAML in few-shot classification via bi-level constraint
Few-shot classification (FSC), which aims to identify novel classes in the presence of a few
labeled samples, has drawn vast attention in recent years. One of the representative few …
labeled samples, has drawn vast attention in recent years. One of the representative few …
MetaDT: Meta decision tree with class hierarchy for interpretable few-shot learning
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with
few examples. Recently, lots of methods have been proposed from the perspective of meta …
few examples. Recently, lots of methods have been proposed from the perspective of meta …
Adaptive federated few-shot feature learning with prototype rectification
M Yang, X Chu, J Zhu, Y Xi, S Niu, Z Wang - Engineering Applications of …, 2023 - Elsevier
Targeting to produce new features from limited data, few-shot feature generation
approaches have attracted extensive attention and successfully mitigated the high cost of …
approaches have attracted extensive attention and successfully mitigated the high cost of …