Real world re-identfication (ReID) algorithms aim to map new observations of
an object to previously recorded instances. These systems are often constrained
by quantity and size of the stored embeddings. To combat this scaling problem,
we attempt to shrink the size of these vectors by using a variety of
compression techniques. In this paper, we benchmark quantization-aware-training
along with three different dimension reduction methods: iterative structured
pruning, slicing the embeddings at initialize, and using low rank embeddings.
We find that ReID embeddings can be compressed by up to 96x with minimal drop
in performance. This implies that modern re-identification paradigms do not
fully leverage the high dimensional latent space, opening up further research
to increase the capabilities of these systems.

这篇论文研究了针对现实世界中物体再识别（ReID）算法的嵌入式压缩技术，通过量化感知训练和三种不同的降维方法（迭代结构化剪枝、初始化时对嵌入进行切片以及使用低秩嵌入）进行了压缩性能评估，发现嵌入可以压缩高达 96 倍，且性能下降较小，这意味着现代再识别方法并没有充分利用高维潜在空间，为进一步提升系统能力开辟了新的研究方向。

用于高效再识别的嵌入式压缩

Embedding Compression for Efficient Re-Identification

Recent studies try to use hyperspectral imaging (HSI) to detect foreign
matters in products because it enables to visualize the invisible wavelengths
including ultraviolet and infrared. Considering the enormous image channels of
the HSI, several dimension reduction methods-e.g., PCA or UMAP-can be
considered to reduce but those cannot ease the fundamental limitations, as
follows: (1) latency of HSI capturing. (2) less explanation ability of the
important channels. In this paper, to circumvent the aforementioned methods,
one of the ways to channel reduction, on anomaly detection proposed HSI.
Different from feature extraction methods (i.e., PCA or UMAP), feature
selection can sort the feature by impact and show better explainability so we
might redesign the task-optimized and cost-effective spectroscopic camera. Via
the extensive experiment results with synthesized MVTec AD dataset, we confirm
that the feature selection method shows 6.90x faster at the inference phase
compared with feature extraction-based approaches while preserving anomaly
detection performance. Ultimately, we conclude the advantage of feature
selection which is effective yet fast.

最近的研究尝试使用高光谱成像（HSI）来检测产品中的异物，因为它能够可视化包括紫外线和红外线在内的不可见波长。本文提出了一种新的特征选择方法，通过绕过传统的维度缩减方法，实现了更好的解释性和捕捉 HSI 的时间延迟问题，从而重新设计了一个任务优化和成本效益的光谱相机。通过对合成 MVTec AD 数据集进行广泛的实验证明，与特征提取方法相比，特征选择方法在推断阶段显示出 6.90 倍的更快速度，同时保持了异常检测的性能。最终，我们得出结论，特征选择方法具有高效且快速的优势。

基于降维方法的高光谱成像异常检测的经验分析

Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using  Dimension Reduction Methods

We present an overview of taxicab correspondence analysis, a robust variant
of correspondence analysis, for visualization of extremely sparse ontingency
tables. In particular we visualize an extremely sparse textual data set of size
590 by 8265 concerning fragments of 8 sacred books recently introduced by Sah
and Fokou\'e (2019) and studied quite in detail by (12 + 1) dimension reduction
methods (t-SNE, UMAP, PHATE,...) by Ma, Sun and Zou (2022).

稀疏交叉表的可视分析方法 —— 出租车对应分析的概述。