Trustworthy Visual-Textual Retrieval.

Visual-textual retrieval, as a link between computer vision and natural language processing, aims at jointly learning visual-semantic relevance to bridge the heterogeneity gap across visual and textual spaces. Existing methods conduct retrieval only relying on the ranking of pairwise similarities, but they cannot self-evaluate the uncertainty of retrieved results, resulting in unreliable retrieval and hindering interpretability. To address this problem, we propose a novel Trust-Consistent Learning framework (TCL) to endow visual-textual retrieval with uncertainty evaluation for trustworthy retrieval. More specifically, TCL first models the matching evidence according to cross-modal similarity to estimate the uncertainty for cross-modal uncertainty-aware learning. Second, a simple yet effective consistency module is presented to enforce the subjective opinions of bidirectional learning to be consistent for high reliability and accuracy. Finally, extensive experiments are conducted to demonstrate the superiority and generalizability of TCL on six widely-used benchmark datasets, i.e., Flickr30K, MS-COCO, MSVD, MSR-VTT, ActivityNet, and DiDeMo. Furthermore, some qualitative experiments are carried out to provide comprehensive and insightful analyses for trustworthy visual-textual retrieval, verifying the reliability and interoperability of TCL. The code is available in https://github.com/QinYang79/TCL.