Most existing object instance detection and segmentation models only work
well on fairly balanced benchmarks where per-category training sample numbers
are comparable, such as COCO. They tend to suffer performance drop on realistic
datasets that are usually long-tailed. This work aims to study and address such
open challenges. Specifically, we systematically investigate performance drop
of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the
recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate
classification of object proposals. Based on such an observation, we first
consider various techniques for improving long-tail classification performance
which indeed enhance instance segmentation results. We then propose a simple
calibration framework to more effectively alleviate classification head bias
with a bi-level class balanced sampling approach. Without bells and whistles,
it significantly boosts the performance of instance segmentation for tail
classes on the recent LVIS dataset and our sampled COCO-LT dataset. Our
analysis provides useful insights for solving long-tail instance detection and
segmentation problems, and the straightforward \emph{SimCal} method can serve
as a simple but strong baseline. With the method we have won the 2019 LVIS
challenge. Codes and models are available at this https URL

本文旨在研究并解决现有的目标检测和分割模型在长尾数据集上的失效问题，通过对现有的两阶段实例分割模型 Mask R-CNN 在最近的长尾 LVIS 数据集上的表现进行系统调研后，我们发现不准确的对象提案分类是其表现下降的主要原因，然后我们提出了一种用于改进长尾分类性能的简单校准框架，它可以更有效地减轻分类头偏差，并结合二级类平衡采样方法。 在我们的实验中表现良好，提高了最近 LVIS 数据集和我们采样的 COCO-LT 数据集的尾部类别实例分割的准确性。

分类中的魔鬼：一种针对长尾物体检测和实例分割的简单框架

The Devil is in Classification: A Simple Framework for Long-tail Object  Detection and Instance Segmentation

In this work, we consider the problem of pedestrian detection in natural
scenes. Intuitively, instances of pedestrians with different spatial scales may
exhibit dramatically different features. Thus, large variance in instance
scales, which results in undesirable large intra-category variance in features,
may severely hurt the performance of modern object instance detection methods.
We argue that this issue can be substantially alleviated by the
divide-and-conquer philosophy. Taking pedestrian detection as an example, we
illustrate how we can leverage this philosophy to develop a Scale-Aware Fast
R-CNN (SAF R-CNN) framework. The model introduces multiple built-in
sub-networks which detect pedestrians with scales from disjoint ranges. Outputs
from all the sub-networks are then adaptively combined to generate the final
detection results that are shown to be robust to large variance in instance
scales, via a gate function defined over the sizes of object proposals.
Extensive evaluations on several challenging pedestrian detection datasets well
demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our
method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and
obtains competitive results on KITTI.

本研究探讨了自然场景下的行人检测问题，提出了基于分治思想的 SAF R-CNN 框架来解决实例尺度的变化范围大导致的特征内类别方差大的问题，并在多个具有挑战性的行人检测数据集上实现了业界领先效果。