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 less investigated. For a large-vocabulary
classifier, the chance of obtaining noisy logits is much higher, which can
easily lead to a wrong recognition. In this paper, we exploit prior knowledge
of the relations among object categories to cluster fine-grained classes into
coarser parent classes, and construct a classification tree that is responsible
for parsing an object instance into a fine-grained category via its parent
class. In the classification tree, as the number of parent class nodes are
significantly less, their logits are less noisy and can be utilized to suppress
the wrong/noisy logits existed in the fine-grained class nodes. As the way to
construct the parent class is not unique, we further build multiple trees to
form a classification forest where each tree contributes its vote to the
fine-grained classification. To alleviate the imbalanced learning caused by the
long-tail phenomena, we propose a simple yet effective resampling method, NMS
Resampling, to re-balance the data distribution. Our method, termed as Forest
R-CNN, can serve as a plug-and-play module being applied to most object
recognition models for recognizing more than 1000 categories. Extensive
experiments are performed on the large vocabulary dataset LVIS. Compared with
the Mask R-CNN baseline, the Forest R-CNN significantly boosts the performance
with 11.5% and 3.9% AP improvements on the rare categories and overall
categories, respectively. Moreover, we achieve state-of-the-art results on the
LVIS dataset. Code is available at this https URL

本研究提出了一种基于分类树和分类森林的方法 Forest R-CNN，通过利用对象类别之间的关系来构建分类树，re-balance 数据分布，以提高在大词汇量数据集 LVIS 上识别 1000 多个类别的目标识别模型的性能。实验表明，此方法在稀有类别和整体类别方面明显提高了性能，并在 LVIS 数据集上实现了最先进的结果。

Forest R-CNN: 大词汇量长尾目标检测和实例分割

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance  Segmentation

With the rapid increase of large-scale, real-world datasets, it becomes
critical to address the problem of long-tailed data distribution (i.e., a few
classes account for most of the data, while most classes are
under-represented). Existing solutions typically adopt class re-balancing
strategies such as re-sampling and re-weighting based on the number of
observations for each class. In this work, we argue that as the number of
samples increases, the additional benefit of a newly added data point will
diminish. We introduce a novel theoretical framework to measure data overlap by
associating with each sample a small neighboring region rather than a single
point. The effective number of samples is defined as the volume of samples and
can be calculated by a simple formula $(1-\beta^{n})/(1-\beta)$, where $n$ is
the number of samples and $\beta \in [0,1)$ is a hyperparameter. We design a
re-weighting scheme that uses the effective number of samples for each class to
re-balance the loss, thereby yielding a class-balanced loss. Comprehensive
experiments are conducted on artificially induced long-tailed CIFAR datasets
and large-scale datasets including ImageNet and iNaturalist. Our results show
that when trained with the proposed class-balanced loss, the network is able to
achieve significant performance gains on long-tailed datasets.

研究长尾数据在机器学习任务中的问题，并提出了使用 “有效样本数” 改进重新权重计算的类平衡损失方法来提高对长尾数据集的训练效果。在人工合成的长尾 CIFAR 数据集和大规模数据集 ImageNet 和 iNaturalist 上进行了全面的实验，并证明该方法能够显著提高长尾数据集上的网络表现。