|
| 1 | +--- |
| 2 | +title: 'Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval' |
| 3 | +description: ICLR 2025 paper |
| 4 | +categories: blog |
| 5 | +--- |
| 6 | + |
| 7 | +*By Mohammad Omama* |
| 8 | + |
| 9 | + |
| 10 | +# Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval |
| 11 | + |
| 12 | +> Proceedings of the International Conference on Learning Representations (ICLR) |
| 13 | +
|
| 14 | + |
| 15 | +`TLDR:` Image Retrieval with Foundation Models: Better, Faster, Distribution-Aware! |
| 16 | + |
| 17 | +[ArXiv](https://arxiv.org/abs/2410.07022) |
| 18 | + |
| 19 | +[Project Website](https://mohdomama.github.io/IRDC-Project-Website/) |
| 20 | + |
| 21 | +## Motivation |
| 22 | + |
| 23 | +Image retrieval is pivotal in many real-world applications, from visual place recognition in robotics to personalized recommendations in e-commerce. However, current state-of-the-art (SOTA) image retrieval methods face two significant problems: |
| 24 | + |
| 25 | +1. **Scalability Issue**: State-of-the-art (SOTA) image retrieval methods train large models separately for each dataset. This is __not scalable__. |
| 26 | + |
| 27 | +2. **Efficiency Issue**: SOTA image retrieval methods use large embeddings, and since retrieval speed is directly proportional to embedding size, this is __not efficient__. |
| 28 | + |
| 29 | +Our research specifically targets these challenges with two crucial questions: |
| 30 | +- **Q1 (Scalability)**: Can we enhance the performance of universal off-the-shelf models in an entirely unsupervised way? |
| 31 | +- **Q2 (Efficiency)**: Is it possible to design an effective unsupervised dimensionality reduction method that preserves the similarity structure and can adaptively perform well at varying embedding sizes? |
| 32 | + |
| 33 | +## Contributions |
| 34 | + |
| 35 | +To tackle the scalability and efficiency challenges, our work introduces the follwoing novel ideas: |
| 36 | + |
| 37 | +- **Autoencoders with Strong Variance Constraints (AE-SVC)**: Addressing scalability, AE-SVC significantly improves off-the-shelf foundation model embeddings through three rigorously enforced constraints: orthogonality, mean-centering, and unit variance in the latent space. We both empirically demonstrate and mathematically validate that these constraints adjust the distribution of cosine similarity, making embeddings more discriminative. |
| 38 | + |
| 39 | +- **Single Shot Similarity Space Distillation ((SS)<sub>2</sub> D)**: To tackle efficiency, (SS)<sub>2</sub> D provides dimensionality reduction that preserves similarity structures |
| 40 | + and further allows embeddings to adaptively scale without retraining. This enables smaller segments of the embedding to retain high retrieval performance, significantly speeding up retrieval. |
| 41 | + |
| 42 | +## Methodology |
| 43 | + |
| 44 | +Our proposed approach follows a two-step pipeline: |
| 45 | + |
| 46 | +1. **AE-SVC** first trains an autoencoder with the constraints mentioned to enhance the embeddings from foundation models. |
| 47 | +2. The improved embeddings from AE-SVC are then distilled using **(SS)<sub>2</sub>D**, producing embeddings that are both efficient and adaptive at various sizes. |
| 48 | + |
| 49 | +The training process ensures that the resulting embeddings, even at smaller sizes, preserve similarity relationships, making them highly effective for retrieval tasks. |
| 50 | + |
| 51 | + |
| 52 | + |
| 53 | +## Impact on Cosine Similarity Distribution |
| 54 | + |
| 55 | +Our AE-SVC method profoundly impacts cosine similarity distributions, significantly reducing their variance. |
| 56 | +Lower variance in similarity distributions correlates with improved discriminative power as we mathematically prove in our paper. |
| 57 | +Our method shows remarkable benefits, particularly for general-purpose foundation models like DINO, compared to already optimized dataset-specific models such as Cosplace. |
| 58 | + |
| 59 | + |
| 60 | + |
| 61 | +## Results |
| 62 | + |
| 63 | +Our experimental validation demonstrates: |
| 64 | + |
| 65 | +- **AE-SVC** consistently surpasses baseline PCA methods across multiple datasets, offering an average of 15.5% improvement in retrieval performance. |
| 66 | +- **(SS)<sub>2</sub>D**, building upon AE-SVC, achieves up to a 10% further improvement at smaller embedding sizes, demonstrating superior performance compared to traditional dimensionality reduction methods like VAE and approaches the theoretical upper bound set by SSD. |
| 67 | + |
| 68 | +This advancement represents a significant step towards more practical, scalable, and efficient image retrieval solutions, enhancing both speed and accuracy. |
| 69 | + |
| 70 | + |
| 71 | + |
0 commit comments