Summary of Embedding Compression For Efficient Re-identification, by Luke Mcdermott
Embedding Compression for Efficient Re-Identification
by Luke McDermott
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles a common challenge in real-world re-identification (ReID) algorithms, which aim to match new observations of an object with previously recorded instances. The issue is that storing and processing these embeddings often requires significant computational resources and memory. To address this scaling problem, the authors explore various compression techniques to reduce the size of these vectors while maintaining performance. They benchmark three dimension reduction methods: iterative structured pruning, slicing the embeddings at initialization, and using low-rank embeddings. The results show that ReID embeddings can be compressed up to 96x with minimal impact on accuracy, suggesting that modern ReID paradigms are not fully utilizing their high-dimensional latent space. This opens up opportunities for future research to further enhance these systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer vision algorithms more efficient by reducing the size of the data they use. Right now, these algorithms need a lot of computing power and memory to work well. The authors tried different ways to shrink the size of this data without losing its importance. They found that some methods can reduce the data size by up to 96 times with very little loss in performance. This means that there is still room for improvement and new ideas to make these algorithms even better. |
Keywords
» Artificial intelligence » Latent space » Pruning
