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 …

Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

Recently, few-shot object detection based on fine-tuning has attracted much attention in the
field of computer vision. However, due to the scarcity of samples in novel categories …

Object detection in remote-sensing images (RSIs) relies on a large amount of labeled data
for training. However, the increasing number of new categories and class imbalance make …

Privacy and memory are two recurring themes in a broad conversation about the societal
impact of AI. These concerns arise from the need for huge amounts of data to train deep …

Federated Learning (FL) has emerged as a potent framework for training models across
distributed data sources while maintaining data privacy. Nevertheless, it faces challenges …

Few-shot object detection (FSOD) identifies objects from extremely few annotated samples.
Most existing FSOD methods, recently, apply the two-stage learning paradigm, which …

In few-shot object detection (FSOD), many approaches retrain the detector in the inference
stage, which is unrealistic in real applications. Moreover, high-quality region proposals are …

Abstract Generalized Few-Shot Object Detection (G-FSOD) seeks to jointly detect base
classes with abundant data and novel classes with limited data. Due to data scarcity …

Adapting object detectors learned with sufficient supervision to novel classes under low data
regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step …