# Cross-modal Zero-shot Hashing by Label Attributes Embedding

arXiv: https://arxiv.org/abs/2111.04080 · HTML: https://mlsanity.com/paper/2111.04080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [Hashing for Similarity Search: A Survey](https://mlsanity.com/paper/1408.2927/) (2014)
- [Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.01223/) (2018)
- [Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.11013/) (2018)
- [Attribute-Guided Network for Cross-Modal Zero-Shot Hashing](https://mlsanity.com/paper/1802.01943/) (2018)
- [Ranking-based Deep Cross-modal Hashing](https://mlsanity.com/paper/1905.04450/) (2019)
- [Weakly-paired Cross-Modal Hashing](https://mlsanity.com/paper/1905.12203/) (2019)
- [Cross-modal Zero-shot Hashing](https://mlsanity.com/paper/1908.07388/) (2019)

## Built on by (most cited first, 1 of 1)

- [Zero-Shot Relational Learning for Multimodal Knowledge Graphs](https://mlsanity.com/paper/2404.06220/) (2024)

## Abstract (arXiv)

Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and testing set are identical. However, the assumption is often violated, causing a zero-shot CMH problem. Recent efforts to address this issue focus on transferring knowledge from the seen classes to the unseen ones using label attributes. However, the attributes are isolated from the features of multi-modal data. To reduce the information gap, we introduce an approach called LAEH (Label Attributes Embedding for zero-shot cross-modal Hashing). LAEH first gets the initial semantic attribute vectors of labels by word2vec model and then uses a transformation network to transform them into a common subspace. Next, it leverages the hash vectors and the feature similarity matrix to guide the feature extraction network of different modalities. At the same time, LAEH uses the attribute similarity as the supplement of label similarity to rectify the label embedding and common subspace. Experiments show that LAEH outperforms related representative zero-shot and cross-modal hashing methods.