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Learning from few examples: A summary of approaches to few-shot learning
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …
from a few training samples. Requiring a large number of data samples, many deep learning …
[HTML][HTML] A survey on few-shot class-incremental learning
Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
Visual prompting via image inpainting
How does one adapt a pre-trained visual model to novel downstream tasks without task-
specific finetuning or any model modification? Inspired by prompting in NLP, this paper …
specific finetuning or any model modification? Inspired by prompting in NLP, this paper …
Grounded language-image pre-training
This paper presents a grounded language-image pre-training (GLIP) model for learning
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …
Base and meta: A new perspective on few-shot segmentation
Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …
generalization capability of most previous works could be fragile when countering hard …
Self-support few-shot semantic segmentation
Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …
support-query matching framework. But they still heavily suffer from the limited coverage of …
A comprehensive survey on source-free domain adaptation
Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …
learning which aims to improve performance on target domains by leveraging knowledge …
Fsce: Few-shot object detection via contrastive proposal encoding
Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …
few training examples, known as few-shot object detection (FSOD). Recent researches …
Defrcn: Decoupled faster r-cnn for few-shot object detection
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few
annotated examples of previously unseen classes, has attracted significant research interest …
annotated examples of previously unseen classes, has attracted significant research interest …
Few-shot object detection with fully cross-transformer
Few-shot object detection (FSOD), with the aim to detect novel objects using very few
training examples, has recently attracted great research interest in the community. Metric …
training examples, has recently attracted great research interest in the community. Metric …