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

To carry out autonomous 3D scanning and online reconstruction of unknown
indoor scenes, one has to find a balance between global exploration of the
entire scene and local scanning of the objects within it. In this work, we
propose a novel approach, which provides object-aware guidance for
autoscanning, for exploring, reconstructing, and understanding an unknown scene
within one navigation pass. Our approach interleaves between object analysis to
identify the next best object (NBO) for global exploration, and object-aware
information gain analysis to plan the next best view (NBV) for local scanning.
First, an objectness-based segmentation method is introduced to extract
semantic objects from the current scene surface via a multi-class graph cuts
minimization. Then, an object of interest (OOI) is identified as the NBO which
the robot aims to visit and scan. The robot then conducts fine scanning on the
OOI with views determined by the NBV strategy. When the OOI is recognized as a
full object, it can be replaced by its most similar 3D model in a shape
database. The algorithm iterates until all of the objects are recognized and
reconstructed in the scene. Various experiments and comparisons have shown the
feasibility of our proposed approach.

提出了一种自主三维扫描的新方法，它通过对象分析确定全局探测的下一个最佳对象（NBO），并通过对象感知信息分析来规划局部扫描的下一个最佳视图（NBV），从而实现了对未知场景的探索、重构和理解。

自主场景重建的物体感知导航

Object-Aware Guidance for Autonomous Scene Reconstruction

This article presents a new distance for measuring shape dissimilarity
between objects. Recent publications introduced the use of eigenvalues of the
Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues
to define a proper distance, called Weighted Spectral Distance (WESD), for
quantifying shape dissimilarity. The definition of WESD is derived through
analysing the heat-trace. This analysis provides the proposed distance an
intuitive meaning and mathematically links it to the intrinsic geometry of
objects. We analyse the resulting distance definition, present and prove its
important theoretical properties. Some of these properties include: i) WESD is
defined over the entire sequence of eigenvalues yet it is guaranteed to
converge, ii) it is a pseudometric, iii) it is accurately approximated with a
finite number of eigenvalues, and iv) it can be mapped to the [0,1) interval.
Lastly, experiments conducted on synthetic and real objects are presented.
These experiments highlight the practical benefits of WESD for applications in
vision and medical image analysis.

本文提出了一种新的距离量，称为加权谱距离（WESD），用于量化物体的形状差异。通过对热传递的分析导出 WESD 的定义，并将 WESD 与物体的内在几何联系起来，证明了其重要的理论性质，最后在合成和真实物体上进行实验验证了 WESD 在视觉和医学图像分析中的实际应用价值。