Joint-teaching: Learning to Refine Knowledge for Resource-constrained Unsupervised Cross-modal Retrieval

Cross-modal retrieval has received considerable attention owing to its applicability to enable users to search desired information with diversified forms. Existing retrieval methods retain good performance mainly relying on complex deep neural networks and high-quality supervision signals, which deters them from real-world resource-constrained development and deployment. In this paper, we propose an effective unsupervised learning framework named JOint-teachinG (JOG) to pursue a high-performance yet light-weight cross-modal retrieval model. The key idea is to utilize the knowledge of a pre-trained model (a.k.a. the "teacher") to endow the to-be-learned model (a.k.a. the "student") with strong feature learning ability and predictive power. Considering that a teacher model serving the same task as the student is not always available, we resort to a cross-task teacher to leverage transferrable knowledge to guide student learning. To eliminate the inevitable noises in the distilled knowledge resulting from the task discrepancy, an online knowledge-refinement strategy is designed to progressively improve the quality of the cross-task knowledge in a joint-teaching manner, where a peer student is engaged. In addition, the proposed JOG learns to represent the original high-dimensional data with compact binary codes to accelerate the query processing, further facilitating resource-limited retrieval. Through extensive experiments, we demonstrate that in various network structures, the proposed method can yield promising learning results on widely-used benchmarks. The proposed research is a pioneering work for resource-constrained cross-modal retrieval, which has strong potential to be applied to on-device deployment and is hoped to pave the way for further study.