Unsupervised Hashing with Similarity Distribution Calibration

ArXiv | Supplementary Material

Official pytorch implementation of the paper: "Unsupervised Hashing with Similarity Distribution Calibration"

BMVC 2023 (Oral)

Description

Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space, due to the limited similarity range of hash codes. The similarity range is bounded by the code length and can lead to a problem known as similarity collapse. That is, the positive and negative pairs of data points become less distinguishable from each other in the hash space. To alleviate this problem, in this paper a novel Simialrity Distribution Calibration (SDC) method is introduced. SDC aligns the hash code similarity distribution towards a calibration distribution (e.g., beta distribution) with sufficient spread across the entire similarity range, thus alleviating the similarity collapse problem. Extensive experiments show that our SDC outperforms significantly the state-of-the-art alternatives on coarse category-level and instance-level image retrieval.

How to run

Training

python main_v2.py model=sdc model.nbit=64 dataset=cifar10 

The above command runs cifar10 with 64-bits settings. We use hydra as config system.

Testing

python val.py model=sdc logdir=/path/to/logdir

The above command runs validation for the trained model, please see val.py to have different validation parameters.

Dataset

Please refer to https://fast-image-retrieval.readthedocs.io/en/latest/dataset.html for dataset downloading.

Feedback

Suggestions and opinions on this work (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to kamwoh.ng at surrey.ac.uk.

Related Works

1. Deep Polarized Network (DPN) - (https://github.com/kamwoh/DPN)
2. One Loss for All (OrthoHash) - (https://github.com/kamwoh/orthohash)
3. Fast Image Retrieval (FIRe) - (https://github.com/CISiPLab/cisip-FIRe)

Implementations

We also implemented (some copied from original authors):

1. Unsupervised GreedyHash
2. BiHalf
3. Weighted Contrastive Hashing
4. Naturally Sort Hash
5. CIBHash

See models/arch, models/trainers and models/loss.

Citation

If you find this work useful for your research, please cite

@inproceedings{ng2023sdc,
  author = {Ng, Kam Woh and Zhu, Xiatian and Hoe, Jiun Tian and Chan, Chee Seng and Zhang, Tianyu and Song, Yi-Zhe and Xiang, Tao},
  booktitle = {British Machine Vision Conference}, 
  title = {Unsupervised Hashing with Similarity Distribution Calibration}, 
  year = {2023}
}