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。