# Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval

arXiv: https://arxiv.org/abs/2404.04998 · HTML: https://mlsanity.com/paper/2404.04998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Central Similarity Quantization for Efficient Image and Video Retrieval](https://mlsanity.com/paper/1908.00347/) (2019)
- [Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval](https://mlsanity.com/paper/1501.06272/) (2015)
- [Deep Triplet Quantization](https://mlsanity.com/paper/1902.00153/) (2019)
- [Weakly Supervised Deep Image Hashing through Tag Embeddings](https://mlsanity.com/paper/1806.05804/) (2018)
- [Learning to Learn from Web Data through Deep Semantic Embeddings](https://mlsanity.com/paper/1808.06368/) (2018)
- [SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation](https://mlsanity.com/paper/1911.08688/) (2019)
- [Deep Spherical Quantization for Image Search](https://mlsanity.com/paper/1906.02865/) (2019)

## Abstract (arXiv)

Deep quantization methods have shown high efficiency on large-scale image retrieval. However, current models heavily rely on ground-truth information, hindering the application of quantization in label-hungry scenarios. A more realistic demand is to learn from inexhaustible uploaded images that are associated with informal tags provided by amateur users. Though such sketchy tags do not obviously reveal the labels, they actually contain useful semantic information for supervising deep quantization. To this end, we propose Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first work to learn deep quantization from weakly tagged images. Specifically, 1) we use word embeddings to represent the tags and enhance their semantic information based on a tag correlation graph. 2) To better preserve semantic information in quantization codes and reduce quantization error, we jointly learn semantics-preserving embeddings and supervised quantizer on hypersphere by employing a well-designed fusion layer and tailor-made loss functions. Extensive experiments show that WSDHQ can achieve state-of-art performance on weakly-supervised compact coding. Code is available at https://github.com/gimpong/AAAI21-WSDHQ.