2024/05/16 by Ziyu Gong, Gong, Ziyu, Chengcheng Mai +3
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Multimedia (cs.MM)
paper · pdf · doi:10.48550/arxiv.2405.10029
openalex publication_date 2024/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The image-text retrieval task aims to retrieve relevant information from a given image or text. The main challenge is to unify multimodal representation and distinguish fine-grained differences across modalities, thereby finding similar contents and filtering irrelevant contents. However, existing methods mainly focus on unified semantic representation and concept alignment for multi-modalities, while the fine-grained differences across modalities have rarely been studied before, making it difficult to solve the information asymmetry problem. In this paper, we propose a novel asymmetry-sensitive contrastive learning method. By generating corresponding positive and negative samples for different asymmetry types, our method can simultaneously ensure fine-grained semantic differentiation and unified semantic representation between multi-modalities. Additionally, a hierarchical cross-modal fusion method is proposed, which integrates global and local-level features through a multimodal attention mechanism to achieve concept alignment. Extensive experiments performed on MSCOCO and Flickr30K, demonstrate the effectiveness and superiority of our proposed method.