# Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network

arXiv: https://arxiv.org/abs/1505.07922 · HTML: https://mlsanity.com/paper/1505.07922/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2015.

## Built on by (most cited first, 30 of 53)

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- [From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval](https://mlsanity.com/paper/2305.10260/) (2023)
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- [Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.12894/) (2023)
- [From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images](https://mlsanity.com/paper/2102.01690/) (2021)
- [Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport](https://mlsanity.com/paper/2402.18411/) (2024)
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- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [LRVS-Fashion: Extending Visual Search with Referring Instructions](https://mlsanity.com/paper/2306.02928/) (2023)
- [Composed Image Retrieval for Training-Free Domain Conversion](https://mlsanity.com/paper/2412.03297/) (2024)
- [Seeing the Abstract: Translating the Abstract Language for Vision Language Models](https://mlsanity.com/paper/2505.03242/) (2025)
- [Enhancing Fine-Grained Classification for Low Resolution Images](https://mlsanity.com/paper/2105.00241/) (2021)
- [Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/2212.04227/) (2022)
- [TextileNet: A Material Taxonomy-based Fashion Textile Dataset](https://mlsanity.com/paper/2301.06160/) (2023)
- [Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval](https://mlsanity.com/paper/2301.13014/) (2022)
- [Cross-view Semantic Alignment for Livestreaming Product Recognition](https://mlsanity.com/paper/2308.04912/) (2023)
- [Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond](https://mlsanity.com/paper/2308.14753/) (2023)
- [Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design](https://mlsanity.com/paper/2311.12067/) (2023)
- [Learning Twofold Heterogeneous Multi-Task by Sharing Similar Convolution Kernel Pairs](https://mlsanity.com/paper/2101.12431/) (2021)
- [ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition](https://mlsanity.com/paper/2105.08643/) (2021)
- [Sentiment Analysis of Fashion Related Posts in Social Media](https://mlsanity.com/paper/2111.07815/) (2021)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)

## Abstract (arXiv)

We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting/pose/background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two sub-networks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).