In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Detecting small or tiny objects is always a difficult and challenging issue in computer vision.
In this paper, we provide a latest and comprehensive survey of deep learning-based …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Detecting tiny objects is a very challenging problem since a tiny object only contains a few
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …

Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …

Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains
a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny …

FPN-based detectors have made significant progress in general object detection, eg, MS
COCO and CityPersons. However, these detectors fail in certain application scenarios, eg …

Accurately and timely detecting multiscale small objects that contain tens of pixels from
remote sensing images (RSI) remains challenging. Most of the existing solutions primarily …

The past few years have witnessed the immense success of object detection, while current
excellent detectors struggle on tackling size-limited instances. Concretely, the well-known …

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors,
especially for label assignment. Despite the exploration of adaptive label assignment in …