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.

翻译：现实世界中的重识别算法旨在将物体的新观测映射到先前记录的实例。这些系统通常受限于存储嵌入的数量和大小。为解决这一扩展性问题，我们尝试通过多种压缩技术来缩减这些向量的大小。本文对量化感知训练以及三种不同的降维方法进行了基准测试：迭代结构化剪枝、初始化时切片嵌入以及使用低秩嵌入。研究发现，重识别嵌入可被压缩高达96倍，同时性能下降极小。这表明现代重识别范式未能充分利用高维潜在空间，为进一步提升这些系统的能力开辟了新的研究方向。