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 …

The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …

Few-shot object detection aims at detecting novel objects with only a few annotated
examples. Prior works have proved meta-learning a promising solution, and most of them …

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 …

Since the propagation of deep neural networks results in the loss of detailed feature
information, the performance of most object detection methods is limited due to their …

Most object detection methods based on remote sensing images are generally dependent
on a large amount of high-quality labeled training data. However, due to the slow acquisition …

Few-shot object detection (FSOD) aims to adapt generic detectors to the novel categories
with only a few annotations, which is an important and realistic task. Although the generic …

Recently, Few-Shot Object Detection (FSOD) has received considerable research attention
as a strategy for reducing reliance on extensively labeled bounding boxes. However, current …

Current Synthetic Aperture Radar (SAR) image object detection methods require huge
amounts of annotated data and can only detect the categories that appears in the training …

Object detection has achieved a huge breakthrough with deep neural networks and massive
annotated data. However, current detection methods cannot be directly transferred to the …