# Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval

arXiv: https://arxiv.org/abs/2001.06657 · HTML: https://mlsanity.com/paper/2001.06657/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch](https://mlsanity.com/paper/2208.03354/) (2022)
- [An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2102.04016/) (2021)
- [Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval](https://mlsanity.com/paper/2202.11948/) (2022)
- [CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval](https://mlsanity.com/paper/2104.09918/) (2021)
- [BDA-SketRet: Bi-Level Domain Adaptation for Zero-Shot SBIR](https://mlsanity.com/paper/2201.06570/) (2022)
- [Zero-Shot Sketch Based Image Retrieval using Graph Transformer](https://mlsanity.com/paper/2201.10185/) (2022)
- [Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions](https://mlsanity.com/paper/2502.08438/) (2025)
- [Towards Unsupervised Sketch-based Image Retrieval](https://mlsanity.com/paper/2105.08237/) (2021)
- [Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2112.07966/) (2021)
- [Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2303.16769/) (2023)

## Abstract (arXiv)

Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training. The assumption may not always be practical since the data of a few classes may be unavailable, or the classes may not appear at the time of training. Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) relaxes this constraint and allows the algorithm to handle previously unseen classes during the test. This paper proposes a generative approach based on the Stacked Adversarial Network (SAN) and the advantage of Siamese Network (SN) for ZS-SBIR. While SAN generates a high-quality sample, SN learns a better distance metric compared to that of the nearest neighbor search. The capability of the generative model to synthesize image features based on the sketch reduces the SBIR problem to that of an image-to-image retrieval problem. We evaluate the efficacy of our proposed approach on TU-Berlin, and Sketchy database in both standard ZSL and generalized ZSL setting. The proposed method yields a significant improvement in standard ZSL as well as in a more challenging generalized ZSL setting (GZSL) for SBIR.