# Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains

arXiv: https://arxiv.org/abs/2108.08356 · HTML: https://mlsanity.com/paper/2108.08356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 23 of 23)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Domain Generalization for Object Recognition with Multi-task Autoencoders](https://mlsanity.com/paper/1508.07680/) (2015)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders](https://mlsanity.com/paper/1812.01784/) (2018)
- [Feature-Critic Networks for Heterogeneous Domain Generalization](https://mlsanity.com/paper/1901.11448/) (2019)
- [Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization](https://mlsanity.com/paper/2007.09316/) (2020)
- [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1904.03451/) (2019)
- [A Zero-Shot Framework for Sketch-based Image Retrieval](https://mlsanity.com/paper/1807.11724/) (2018)
- [Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval](https://mlsanity.com/paper/1703.05605/) (2017)
- [Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1903.03372/) (2019)
- [Towards Recognizing Unseen Categories in Unseen Domains](https://mlsanity.com/paper/2007.12256/) (2020)
- [Zero-Shot Sketch-Image Hashing](https://mlsanity.com/paper/1803.02284/) (2018)
- [Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/1904.03208/) (2019)
- [Zero-Shot Learning via Joint Latent Similarity Embedding](https://mlsanity.com/paper/1511.04512/) (2015)
- [Memory-Based Neighbourhood Embedding for Visual Recognition](https://mlsanity.com/paper/1908.04992/) (2019)
- [Zero Shot Domain Generalization](https://mlsanity.com/paper/2008.07443/) (2020)
- [Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2003.09869/) (2020)
- [Open Cross-Domain Visual Search](https://mlsanity.com/paper/1911.08621/) (2019)

## Built on by (most cited first, 6 of 6)

- [MADG: Margin-based Adversarial Learning for Domain Generalization](https://mlsanity.com/paper/2311.08503/) (2023)
- [Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval](https://mlsanity.com/paper/2302.06081/) (2023)
- [ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval](https://mlsanity.com/paper/2312.12478/) (2023)
- [Composed Image Retrieval for Training-Free Domain Conversion](https://mlsanity.com/paper/2412.03297/) (2024)
- [UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval](https://mlsanity.com/paper/2412.10680/) (2024)
- [Test-time Training for Data-efficient UCDR](https://mlsanity.com/paper/2208.09198/) (2022)

## Abstract (arXiv)

In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training. Due to dynamically increasing number of categories and practical constraint of training on every possible domain, which requires large amounts of data, generalizing to both unseen classes and domains is important. Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing. Specifically, we introduce a novel Semantic Neighborhood loss to bridge the knowledge gap between seen and unseen classes and ensure that the latent space embedding of the unseen classes is semantically meaningful with respect to its neighboring classes. We also introduce a mix-up based supervision at image-level as well as semantic-level of the data for training with the Mixture Prediction loss, which helps in efficient retrieval when the query belongs to an unseen domain. These losses are incorporated on the SE-ResNet50 backbone to obtain SnMpNet. Extensive experiments on two large-scale datasets, Sketchy Extended and DomainNet, and thorough comparisons with state-of-the-art justify the effectiveness of the proposed model.