Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …

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 …

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 …

Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …

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 …

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 …

Detecting rare objects from a few examples is an emerging problem. Prior works show meta-
learning is a promising approach. But, fine-tuning techniques have drawn scant attention …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training
instances, and is useful when manual annotation is time-consuming or data acquisition is …