Retrieval Across Any Domains via Large-scale Pre-trained Model
ICML, 2024

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.

Included in review: No