# Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax

arXiv: https://arxiv.org/abs/1812.04275 · HTML: https://mlsanity.com/paper/1812.04275/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild](https://mlsanity.com/paper/2207.04945/) (2022)
- [ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2111.12757/) (2021)
- [Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval](https://mlsanity.com/paper/2401.04860/) (2024)
- [Structure Similarity Preservation Learning for Asymmetric Image Retrieval](https://mlsanity.com/paper/2403.00648/) (2024)
- [Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation](https://mlsanity.com/paper/2605.10009/) (2026)

## Abstract (arXiv)

This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation. Inspired by channel-wise attention explored in recent years, we present a Domain-Aware Squeeze-and-Excitation (DASE) network, which seamlessly incorporates the prior knowledge of sample sketch or photo into SE module and make the SE module capable of emphasizing appropriate channels according to domain signal. Accordingly, the proposed network can switch its mode to achieve a better domain feature with lower intra-class discrepancy. Moreover, while previous works simply focus on minimizing intra-class distance and maximizing inter-class distance, we introduce a loss function, named Multiplicative Euclidean Margin Softmax (MEMS), which introduces multiplicative Euclidean margin into feature space and ensure that the maximum intra-class distance is smaller than the minimum inter-class distance. This facilitates learning a highly discriminative feature space and ensures a more accurate image retrieval result. Extensive experiments are conducted on two widely used SBIR benchmark datasets. Our approach achieves better results on both datasets, surpassing the state-of-the-art methods by a large margin.