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 …

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 …

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 …

Two-stage object detectors generate object proposals and classify them to detect objects in
images. These proposals often do not perfectly contain the objects but overlap with them in …

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 …

The majority of few-shot object detection methods use a shared feature map for both
classification and localization, despite the conflicting requirements of these two tasks …

Object detection is a critical field in computer vision focusing on accurately identifying and
locating specific objects in images or videos. Traditional methods for object detection rely on …

One-shot object detection (OSOD) without fine-tuning has recently garnered considerable
attention and research focus. It aims to directly detect novel-class objects in the target image …

Deep learning-based object detectors often demand abundant annotated data for training.
However, in practice, only limited training data are available, making Few-Shot Object …

Abstract Few-Shot Object Detection (FSOD) is a significant application of few-shot learning
in object detection tasks. Its primary objective is to enable the model to quickly acquire the …