Grunnleggende konsepter
The author proposes the Cross-modal and Uni-modal Soft-label Alignment (CUSA) method to address inter-modal matching missing and intra-modal semantic loss problems in image-text retrieval, leveraging uni-modal pre-trained models for soft-label supervision signals.
Sammendrag
The content introduces the challenges faced by current image-text retrieval methods and presents a novel approach, CUSA, to improve performance. It discusses the importance of addressing inter-modal matching missing and intra-modal semantic loss problems through soft-label alignment techniques.
Current image-text retrieval methods face challenges like inter-modal matching missing and intra-modal semantic loss. The proposed CUSA method leverages uni-modal pre-trained models for soft-label supervision signals. It aims to enhance similarity recognition between uni-modal samples and improve overall performance.
The paper provides detailed explanations of the problems faced by existing methods, the proposed solution using CUSA, and extensive experimental results showcasing improved performance in various image-text retrieval models.
Key points include the introduction of CUSA method, explanation of inter-modal matching missing problem, discussion on intra-modal semantic loss problem, details on CSA and USA alignment techniques, demonstration of improved results through experiments, and insights into universal retrieval capabilities achieved by the method.
Statistikk
Current image-text retrieval methods have demonstrated impressive performance.
Proposed method called Cross-modal and Uni-modal Soft-label Alignment (CUSA).
Extensive experiments demonstrate consistent improvement in performance.
Achieved new state-of-the-art results in image-text retrieval.
Improved uni-modal retrieval performance enabling universal retrieval.
Sitater
"Our method leverages uni-modal pre-training models to provide soft-label supervision signals."
"Our method can consistently improve the performance of image-text retrieval."