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 …

Humans are able to learn to recognize new objects even from a few examples. In contrast,
training deep-learning-based object detectors requires huge amounts of annotated data. To …

Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by
the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed …

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 …

Fusing infrared and visible images can provide more texture details for subsequent object
detection task. Conversely, detection task furnishes object semantic information to improve …

Few-shot object detection (FSOD) aims to detect never-seen objects using few examples.
This field sees recent improvement owing to the meta-learning techniques by learning how …

Few-shot aerial image object detection aims to rapidly detect object instances of novel
category in aerial images by using few labeled samples. However, due to the complex …

Abstract Vision Transformers (ViTs) emerge to achieve impressive performance on many
data-abundant computer vision tasks by capturing long-range dependencies among local …

Generalized few-shot object detection aims to achieve precise detection on both base
classes with abundant annotations and novel classes with limited training data. Existing …

Few-shot Learning has been studied to mimic human visual capabilities and learn effective
models without the need of exhaustive human annotation. Even though the idea of meta …