Benchmarks are used for testing new optimization algorithms and their
variants to evaluate their performance. Most existing benchmarks are smooth
functions. This chapter introduces ten new benchmarks with different
properties, including noise, discontinuity, parameter estimation and unknown
paths.

本研究介绍了十个具有不同特性（包括噪声、不连续性、参数估计和未知路径）的新基准，用于测试新优化算法及其变种以评估其性能。

优化的十个新基准

Ten New Benchmarks for Optimization

Person recognition methods that use multiple body regions have shown
significant improvements over traditional face-based recognition. One of the
primary challenges in full-body person recognition is the extreme variation in
pose and view point. In this work, (i) we present an approach that tackles pose
variations utilizing multiple models that are trained on specific poses, and
combined using pose-aware weights during testing. (ii) For learning a person
representation, we propose a network that jointly optimizes a single loss over
multiple body regions. (iii) Finally, we introduce new benchmarks to evaluate
person recognition in diverse scenarios and show significant improvements over
previously proposed approaches on all the benchmarks including the photo album
setting of PIPA.

本研究提出多个模型联合训练并在测试中使用姿势感知权重的方法解决全身人物识别中的姿态变化问题。同时，使用网络对多个身体区域进行联合优化，为解决不同场景下的人物识别问题提出了新的基准测试，并证明了该方法在 PIPA 数据集中取得的高准确率。