Text-image cross-modal retrieval is a challenging task in the field of language and vision. Most previous approaches independently embed images and sentences into a joint embedding space and compare their similarities. However, previous approaches rarely explore the interactions between images and sentences before calculating similarities in the joint space. Intuitively, when matching between images and sentences, human beings would alternatively attend to regions in images and words in sentences, and select the most salient information considering the interaction between both modalities. In this paper, we propose Cross-modal Adaptive Message Passing (CAMP), which adaptively controls the information flow for message passing across modalities. Our approach not only takes comprehensive and fine-grained cross-modal interactions into account, but also properly handles negative pairs and irrelevant information with an adaptive gating scheme. Moreover, instead of conventional joint embedding approaches for text-image matching, we infer the matching score based on the fused features, and propose a hardest negative binary cross-entropy loss for training. Results on COCO and Flickr30k significantly surpass state-of-the-art methods, demonstrating the effectiveness of our approach.

本文提出了一种跨模态自适应信息传递模型（Cross-modal Adaptive Message Passing, CAMP），通过自适应地控制跨模态信息的流动，全面且精细地考虑跨模态交互，使用硬负样本二元交叉熵损失函数进行训练，在 COCO 和 Flickr30k 上显著超过最先进方法，显示出了我们方法的有效性。

CAMP: 文本-图像检索的跨模态自适应消息传递