In this paper, we address the text and image matching in cross-modal retrieval of the fashion
industry. Different from the matching in the general domain, the fashion matching is required …

Cross-modal retrieval aims to identify relevant data across different modalities. In this work,
we are dedicated to cross-modal retrieval between images and text sentences, which is …

Despite the evolution of deep-learning-based visual-textual processing systems, precise
multi-modal matching remains a challenging task. In this work, we tackle the task of cross …

Generally, most existing cross-modal retrieval methods only consider global or local
semantic embeddings, lacking fine-grained dependencies between objects. At the same …

Cross-modal image-text retrieval has been a long-standing challenge in the multimedia
community. Existing methods explore various complicated embedding spaces to assess the …

The abundance of multimodal data (eg social media posts) has inspired interest in cross-
modal retrieval methods. Popular approaches rely on a variety of metric learning losses …

Enabling bi-directional retrieval of images and texts is important for understanding the
correspondence between vision and language. Existing methods leverage the attention …

Traditionally, the task of cross-modal retrieval is tackled through joint embedding. However,
the global matching used in joint embedding methods often fails to effectively describe …

Cross-model retrieval has attracted much attention in recent years due to its wide
applications. Conventional approaches usually take one modality as query to retrieve …

Cross-modal retrieval, eg, using an image query to search related text and vice-versa, has
become a highlighted research topic, to provide flexible retrieval experience across multi …