Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
This addresses the challenge of matching images and sentences with complex content for applications in computer vision and NLP, representing an incremental improvement over existing methods.
The paper tackled the problem of textual-visual cross-modal retrieval by proposing a generative model to learn both global and local features, achieving state-of-the-art results on the MSCOCO dataset.
Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.