# Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval

arXiv: https://arxiv.org/abs/2106.11841 · HTML: https://mlsanity.com/paper/2106.11841/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [Label-Embedding for Image Classification](https://mlsanity.com/paper/1503.08677/) (2015)
- [Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/1704.08345/) (2017)
- [Multi-modal Cycle-consistent Generalized Zero-Shot Learning](https://mlsanity.com/paper/1808.00136/) (2018)
- [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)
- [Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2002.10310/) (2020)
- [Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1903.03372/) (2019)
- [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 Hashing via Transferring Supervised Knowledge](https://mlsanity.com/paper/1606.05032/) (2016)
- [Incremental Embedding Learning via Zero-Shot Translation](https://mlsanity.com/paper/2012.15497/) (2020)
- [Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2003.09869/) (2020)

## Built on by (most cited first, 13 of 13)

- [Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style](https://mlsanity.com/paper/2303.14348/) (2023)
- [ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2111.12757/) (2021)
- [Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2305.05144/) (2023)
- [Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning](https://mlsanity.com/paper/2407.04207/) (2024)
- [Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2204.05666/) (2022)
- [Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2303.16769/) (2023)
- [Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval](https://mlsanity.com/paper/2407.00979/) (2024)
- [Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2301.06685/) (2023)
- [Relation-Aware Meta-Learning for Zero-shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2412.00120/) (2024)
- [Ontology-aware Network for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2302.10040/) (2023)
- [Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2312.10320/) (2023)
- [Dynamic Multi-level Weighted Alignment Network for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2511.00925/) (2025)
- [Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation](https://mlsanity.com/paper/2605.10009/) (2026)

## Abstract (arXiv)

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets. Our source code is publicly available at https://github.com/haowang1992/DSN.