We present an unsupervised data-driven approach for non-rigid shape matching.
Shape matching identifies correspondences between two shapes and is a
fundamental step in many computer vision and graphics applications. Our
approach is designed to be particularly robust when matching shapes digitized
using 3D scanners that contain fine geometric detail and suffer from different
types of noise including topological noise caused by the coalescence of
spatially close surface regions. We build on two strategies. First, using a
hierarchical patch based shape representation we match shapes consistently in a
coarse to fine manner, allowing for robustness to noise. This multi-scale
representation drastically reduces the dimensionality of the problem when
matching at the coarsest scale, rendering unsupervised learning feasible.
Second, we constrain this hierarchical matching to be reflected in 3D by
fitting a patch-wise near-rigid deformation model. Using this constraint, we
leverage spatial continuity at different scales to capture global shape
properties, resulting in matchings that generalize well to data with different
deformations and noise characteristics. Experiments demonstrate that our
approach obtains significantly better results on raw 3D scans than
state-of-the-art methods, while performing on-par on standard test scenarios.

我们提出了一种无监督数据驱动的方法用于非刚性形状匹配，通过建立层次化基于补丁的形状表示和约束形状匹配的 3D 近刚性变形模型，实现了对噪声和变形具有鲁棒性的匹配。实验证明，相比最先进的方法在原始 3D 扫描数据上获得了显著更好的结果，并在标准测试场景上表现相当。

基于变形引导的非刚性形状匹配无监督方法

Deformation-Guided Unsupervised Non-Rigid Shape Matching

Sequential Recommender Systems (SRSs) are a popular type of recommender
system that learns from a user's history to predict the next item they are
likely to interact with. However, user interactions can be affected by noise
stemming from account sharing, inconsistent preferences, or accidental clicks.
To address this issue, we (i) propose a new evaluation protocol that takes
multiple future items into account and (ii) introduce a novel relevance-aware
loss function to train a SRS with multiple future items to make it more robust
to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10
and 0.88% in the traditional evaluation protocol, while in the new evaluation
protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best
performing models.

提出了一个新的评估协议来解决 “多余噪声” 对顺序推荐系统的影响，并引入了一种新型的关联感知损失函数来训练该系统，使其更具有鲁棒性，该模型在传统评估协议中 NDCG@10 有 0.88% 的提高，在新评估协议中有 1.63% 的 NDCG@10 和 1.5％的 HR 提高。

序列推荐系统中训练损失中集成项目相关性

Integrating Item Relevance in Training Loss for Sequential Recommender  Systems

As facial recognition systems are deployed more widely, scholars and
activists have studied their biases and harms. Audits are commonly used to
accomplish this and compare the algorithmic facial recognition systems'
performance against datasets with various metadata labels about the subjects of
the images. Seminal works have found discrepancies in performance by gender
expression, age, perceived race, skin type, etc. These studies and audits often
examine algorithms which fall into two categories: academic models or
commercial models. We present a detailed comparison between academic and
commercial face detection systems, specifically examining robustness to noise.
We find that state-of-the-art academic face detection models exhibit
demographic disparities in their noise robustness, specifically by having
statistically significant decreased performance on older individuals and those
who present their gender in a masculine manner. When we compare the size of
these disparities to that of commercial models, we conclude that commercial
models - in contrast to their relatively larger development budget and
industry-level fairness commitments - are always as biased or more biased than
an academic model.

本文研究了基于算法的面部识别系统的偏差和损害，通过比较学术模型和商业模型的噪声鲁棒性发现，学术模型在年龄和男性化性别方面的表现存在明显的种族和性别偏见，与商业模型相比，商业模型在公平性方面的表现并不理想。