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 …

Crop counting is a crucial step in crop yield estimation. By counting, crop growth status can
be accurately detected and adjusted, improving crop yield and quality. In recent years, with …

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 …

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), with the aim to detect novel objects using very few
training examples, has recently attracted great research interest in the community. Metric …

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 …

As few-shot object detectors are often trained with abundant base samples and fine-tuned
on few-shot novel examples, the learned models are usually biased to base classes and …

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

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 object detection (FSOD) aims to detect objects with only a few training examples.
Visual feature extraction and query-support similarity learning are the two critical …