Coarse-to-fine 3D instance segmentation methods show weak performances
compared to recent Grouping-based, Kernel-based and Transformer-based methods.
We argue that this is due to two limitations: 1) Instance size overestimation
by axis-aligned bounding box(AABB) 2) False negative error accumulation from
inaccurate box to the refinement phase. In this work, we introduce Spherical
Mask, a novel coarse-to-fine approach based on spherical representation,
overcoming those two limitations with several benefits. Specifically, our
coarse detection estimates each instance with a 3D polygon using a center and
radial distance predictions, which avoids excessive size estimation of AABB. To
cut the error propagation in the existing coarse-to-fine approaches, we
virtually migrate points based on the polygon, allowing all foreground points,
including false negatives, to be refined. During inference, the proposal and
point migration modules run in parallel and are assembled to form binary masks
of instances. We also introduce two margin-based losses for the point migration
to enforce corrections for the false positives/negatives and cohesion of
foreground points, significantly improving the performance. Experimental
results from three datasets, such as ScanNetV2, S3DIS, and STPLS3D, show that
our proposed method outperforms existing works, demonstrating the effectiveness
of the new instance representation with spherical coordinates.

提出一种基于球面表示的新型粗糙到精细方法，在 3D 实例分割中克服了轴对齐边界框 AABB 的实例尺寸估计过高和从不精确的边界框到细化阶段的假阴性误差累积两个局限性，通过引入两种基于边界框迁移的边界框检测和点迁移模块并运用两种基于间隔的损失函数来改善结果。实验证明，在三个数据集上，该方法优于现有工作，在使用球面坐标的新实例表示方法方面具有显著的有效性。

球形掩模：基于球形表示的粗到细的 3D 点云实例分割

Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with  Spherical Representation

Face recognition has made tremendous progress in recent years due to the
advances in loss functions and the explosive growth in training sets size. A
properly designed loss is seen as key to extract discriminative features for
classification. Several margin-based losses have been proposed as alternatives
of softmax loss in face recognition. However, two issues remain to consider: 1)
They overlook the importance of hard sample mining for discriminative learning.
2) Label noise ubiquitously exists in large-scale datasets, which can seriously
damage the model's performance. In this paper, starting from the perspective of
decision boundary, we propose a novel mining framework that focuses on the
relationship between a sample's ground truth class center and its nearest
negative class center. Specifically, a closed-set noise label self-correction
module is put forward, making this framework work well on datasets containing a
lot of label noise. The proposed method consistently outperforms SOTA methods
in various face recognition benchmarks. Training code has been released at
this https URL

从决策边界的角度出发，我们提出了一种新的采矿框架，该框架关注样本的基本真实类中心和其最近的负类中心之间的关系，特别是提出了一个闭集噪声标签自校正模块来处理包含大量噪声标签的数据集，在各种面部识别基准测试中，所提出的方法始终优于 SOTA 方法。

BoundaryFace：面部识别噪声标签自校正挖掘框架

BoundaryFace: A mining framework with noise label self-correction for  Face Recognition

The top-k error is a common measure of performance in machine learning and
computer vision. In practice, top-k classification is typically performed with
deep neural networks trained with the cross-entropy loss. Theoretical results
indeed suggest that cross-entropy is an optimal learning objective for such a
task in the limit of infinite data. In the context of limited and noisy data
however, the use of a loss function that is specifically designed for top-k
classification can bring significant improvements. Our empirical evidence
suggests that the loss function must be smooth and have non-sparse gradients in
order to work well with deep neural networks. Consequently, we introduce a
family of smoothed loss functions that are suited to top-k optimization via
deep learning. The widely used cross-entropy is a special case of our family.
Evaluating our smooth loss functions is computationally challenging: a na\"ive
algorithm would require $\mathcal{O}(\binom{n}{k})$ operations, where n is the
number of classes. Thanks to a connection to polynomial algebra and a
divide-and-conquer approach, we provide an algorithm with a time complexity of
$\mathcal{O}(k n)$. Furthermore, we present a novel approximation to obtain
fast and stable algorithms on GPUs with single floating point precision. We
compare the performance of the cross-entropy loss and our margin-based losses
in various regimes of noise and data size, for the predominant use case of k=5.
Our investigation reveals that our loss is more robust to noise and overfitting
than cross-entropy.

论文研究了深度神经网络中 Top-k 分类任务的性能评估方法，提出了一族平滑损失函数，与交叉熵类似但更适用于 Top-k 优化，其中一种基于边界的新型损失函数在处理噪声和数据大小等多种情况下比交叉熵更有鲁棒性。