An efficient framework for zero-shot sketch-based image retrieval

Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it’s real-world applications, and the more realistic and challenging setting than found in SBIR. ZS-SBIR inherits the main challenges of multiple computer vision problems including contentbased Image Retrieval (CBIR), zero-shot learning and domain adaptation. The majority of previous studies using deep neural networks have achieved improved results through either projecting sketch and images into a common low-dimensional space or transferring knowledge from seen to unseen classes. However, those approaches are trained with complex frameworks composed of multiple deep convolutional neural networks (CNNs) and are dependent on category-level word labels. This increases the requirements on training resources and datasets. In comparison, we propose a simple and efficient framework that does not require high computational training resources, and can be trained on datasets without semantic categorical labels. Furthermore, at training and inference stages our method only uses a single CNN. In this work, a pre-trained ImageNet CNN (i.e.ResNet50) is fine-tuned with three proposed learning objects: domain-aware quadruplet loss, semantic classification loss, and semantic knowledge preservation loss. The domain-aware quadruplet and semantic classification losses are introduced to learn discriminative, semantic and domain invariant features through considering ZS-SBIR as a object detection and verification problem. To preserve semantic knowledge learned with ImageNet and utilise it on unseen categories, the semantic knowledge preservation loss is proposed. To reduce computational cost and increase the accuracy of the semantic knowledge distillation process, ground-truth semantic knowledge is prepared in a class-oriented fashion prior to training. Extensive experiments are conducted on three challenging ZS-SBIR datasets, Sketchy Extended, TU-Berlin Extended and QuickDraw Extended. The proposed method achieves state-of-the-art results, and outperforms the majority of related works by a large margin.