# Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

arXiv: https://arxiv.org/abs/1904.03208 · HTML: https://mlsanity.com/paper/1904.03208/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 30 of 40)

- [MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions](https://mlsanity.com/paper/2403.19651/) (2024)
- [CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not](https://mlsanity.com/paper/2303.13440/) (2023)
- [Sketch3T: Test-Time Training for Zero-Shot SBIR](https://mlsanity.com/paper/2203.14691/) (2022)
- [A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch](https://mlsanity.com/paper/2208.03354/) (2022)
- [Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style](https://mlsanity.com/paper/2303.14348/) (2023)
- [An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2102.04016/) (2021)
- [Adaptive Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2207.01723/) (2022)
- [Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2106.11841/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (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)
- [One Sketch for All: One-Shot Personalized Sketch Segmentation](https://mlsanity.com/paper/2112.10838/) (2021)
- [BDA-SketRet: Bi-Level Domain Adaptation for Zero-Shot SBIR](https://mlsanity.com/paper/2201.06570/) (2022)
- [Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers](https://mlsanity.com/paper/2403.07214/) (2024)
- [Zero-Shot Sketch Based Image Retrieval using Graph Transformer](https://mlsanity.com/paper/2201.10185/) (2022)
- [Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains](https://mlsanity.com/paper/2108.08356/) (2021)
- [Federated Action Recognition on Heterogeneous Embedded Devices](https://mlsanity.com/paper/2107.12147/) (2021)
- [Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval](https://mlsanity.com/paper/2310.08009/) (2023)
- [Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors](https://mlsanity.com/paper/2401.16459/) (2024)
- [SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models](https://mlsanity.com/paper/2503.14129/) (2025)
- [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)
- [Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval](https://mlsanity.com/paper/2309.08372/) (2023)
- [Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning](https://mlsanity.com/paper/2407.04207/) (2024)
- [Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2112.07966/) (2021)
- [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)
- [If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval](https://mlsanity.com/paper/2303.17703/) (2023)
- [Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2401.04860/) (2024)
- [Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval](https://mlsanity.com/paper/2407.00979/) (2024)

## Abstract (arXiv)

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in solving this problem under the more realistic and challenging setting of zero-shot learning. In this paper, we investigate this problem from the viewpoint of domain adaptation which we show is critical in improving feature embedding in the zero-shot scenario. Based on a framework which starts with a pre-trained model on ImageNet and fine-tunes it on the training set of SBIR benchmark, we advocate the importance of preserving previously acquired knowledge, e.g., the rich discriminative features learned from ImageNet, to improve the model's transfer ability. For this purpose, we design an approach named Semantic-Aware Knowledge prEservation (SAKE), which fine-tunes the pre-trained model in an economical way and leverages semantic information, e.g., inter-class relationship, to achieve the goal of knowledge preservation. Zero-shot experiments on two extended SBIR datasets, TU-Berlin and Sketchy, verify the superior performance of our approach. Extensive diagnostic experiments validate that knowledge preserved benefits SBIR in zero-shot settings, as a large fraction of the performance gain is from the more properly structured feature embedding for photo images. Code is available at: https://github.com/qliu24/SAKE.