Cross-Modal Guided Visual Representation Learning for Social Image Retrieval

Abstract

Social images are often associated with rich but noisy tags from community contributions. Although social tags can potentially provide valuable semantic training information for image retrieval, existing studies all fail to effectively filter noises by exploiting the cross-modal correlation between image content and tags. The current cross-modal vision-and-language representation learning methods, which selectively attend to the relevant parts of the image and text, show a promising direction. However, they are not suitable for social image retrieval since: (1) they deal with natural text sequences where the relationships between words can be easily captured by language models for cross-modal relevance estimation, while the tags are isolated and noisy; (2) they take (image, text) pair as input, and consequently cannot be employed directly for unimodal social image retrieval. This paper tackles the challenge of utilizing cross-modal interactions to learn precise representations for unimodal retrieval. The proposed framework, dubbed CGVR (Cross-modal Guided Visual Representation), extracts accurate semantic representations of images from noisy tags and transfers this ability to image-only hashing subnetwork by a carefully designed training scheme. To well capture correlated semantics and filter noises, it embeds a priori common-sense relationship among tags into attention computation for joint awareness of textual and visual context. Experiments show that CGVR achieves approximately 8.82 and 5.45 points improvement in MAP over the state-of-the-art on two widely used social image benchmarks. CGVR can serve as a new baseline for the image retrieval community.