# A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval

arXiv: https://arxiv.org/abs/1711.06016 · HTML: https://mlsanity.com/paper/1711.06016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining](https://mlsanity.com/paper/2107.14572/) (2021)
- [Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval](https://mlsanity.com/paper/2206.08842/) (2022)

## Abstract (arXiv)

There is a growing trend in studying deep hashing methods for content-based image retrieval (CBIR), where hash functions and binary codes are learnt using deep convolutional neural networks and then the binary codes can be used to do approximate nearest neighbor (ANN) search. All the existing deep hashing papers report their methods' superior performance over the traditional hashing methods according to their experimental results. However, there are serious flaws in the evaluations of existing deep hashing papers: (1) The datasets they used are too small and simple to simulate the real CBIR situation. (2) They did not correctly include the search time in their evaluation criteria, while the search time is crucial in real CBIR systems. (3) The performance of some unsupervised hashing algorithms (e.g., LSH) can easily be boosted if one uses multiple hash tables, which is an important factor should be considered in the evaluation while most of the deep hashing papers failed to do so. We re-evaluate several state-of-the-art deep hashing methods with a carefully designed experimental setting. Empirical results reveal that the performance of these deep hashing methods are inferior to multi-table IsoH, a very simple unsupervised hashing method. Thus, the conclusions in all the deep hashing papers should be carefully re-examined.