Despite the previous success of object analysis, detecting and segmenting a large number
of object categories with a long-tailed data distribution remains a challenging problem and is …

Object recognition techniques using convolutional neural networks (CNN) have achieved
great success. However, state-of-the-art object detection methods still perform poorly on …

Large vocabulary object detectors are often faced with the long-tailed label distributions,
seriously degrading their ability to detect rarely seen categories. On one hand, the rare …

To address the problem of long-tail distribution for the large vocabulary object detection task,
existing methods usually divide the whole categories into several groups and treat each …

Solving long-tail large vocabulary object detection with deep learning based models is a
challenging and demanding task, which is however under-explored. In this work, we provide …

Conventional detectors suffer from performance degradation when dealing with long-tailed
data due to a classification bias towards the majority head categories. In this article, we …

Long-tailed class distributions are prevalent among the practical applications of object
detection and instance segmentation. Prior work in long-tail instance segmentation …

Training on datasets with long-tailed distributions has been challenging for major
recognition tasks such as classification and detection. To deal with this challenge, image …

Many objects do not appear frequently enough in complex scenes (eg, certain handbags in
living rooms) for training an accurate object detector, but are often found frequently by …

Despite the recent success of long-tailed object detection, almost all long-tailed object
detectors are developed based on the two-stage paradigm. In practice, one-stage detectors …