In recent years, many methods have been proposed to address the zero-shot sketch-based image retrieval (ZS-SBIR) task, which is a practical problem in many applications. However, in real-world scenarios, on the one hand, we can not obtain training data with the same distribution as the test data, and on the other hand, the labels of training data are not available as usual. To tackle this issue, we focus on a new problem, namely unsupervised zero-shot sketch-based image retrieval (UZS-SBIR), where the available training data does not have labels while the training and testing categories are not overlapping. In this paper, we introduce a new asymmetric mutual alignment method (AMA) including a self-distillation module and a cross-modality mutual alignment module. First, we conduct self-distillation to extract the feature embeddings from unlabeled data. Due to the lack of available information in an unsupervised manner, we employ the cross-modality mutual alignment module to further excavate underlying intra-modality and inter-modality relationships from unlabeled data, and take full advantage of these correlations to align the feature embeddings in image and sketch domains. Meanwhile, the feature representations are enhanced by the intra-modality clustering relations, leading to better generalization ability to unseen classes. Moreover, we conduct an asymmetric strategy to update the teacher and student networks, respectively. Extensive experimental results on several benchmark datasets demonstrate the superiority of our method.

In order to enhance the generalization ability towards unseen domains, universal cross-domain image retrieval methods require a training dataset encompassing diverse domains, which is costly to assemble. Given this constraint, we introduce a novel problem of data-free adaptive cross-domain retrieval, eliminating the need for real images during training. Towards this goal, we propose a novel Text-driven Knowledge Integration (TKI) method, which exclusively utilizes a pre-trained vision-language model to implement an “aggregation after expansion" training strategy. Specifically, we extract diverse implicit domain-specific information through a set of learnable domain word vectors. Subsequently, a domain-agnostic universal projection, equipped with a non-Euclidean multi-layer perceptron, can be optimized using these assorted text descriptions through the text-proxied domain aggregation. Leveraging the cross-modal transferability phenomenon of the shared latent space, we can integrate the trained domain-agnostic universal projection with the pre-trained visual encoder to extract the features of the input image for the following retrieval during testing. Extensive experimental results on several benchmark datasets demonstrate the superiority of our method.

Exploring good generalization ability is essential in deep metric learning (DML). Most existing DML methods focus on improving the model robustness against category shift to keep the performance on unseen categories. However, in addition to category shift, domain shift also widely exists in real-world scenarios. Therefore, learning better generalization ability for the DML model is still a challenging yet realistic problem. In this paper, we propose a new self-expanded equalization (SEE) method to effectively generalize the DML model to both unseen categories and domains. Specifically, we take a ‘min-max’ strategy combined with a proxy-based loss to adaptively augment diverse out-of-distribution samples that vastly expand the span of original training data. To take full advantage of the implicit cross-domain relations between source and augmented samples, we introduce a domain-aware equalization module to induce the domain-invariant distance metric by regularizing the feature distribution in the metric space. Extensive experiments on two benchmarks and a large-scale multi-domain dataset demonstrate the superiority of our SEE over the existing DML methods.