Cross-modal retrieval aims to realize accurate and flexible retrieval across different
modalities of data, eg, image and text, which has achieved significant progress in recent …

DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities
as image and text, but existing methods often face the challenge of insufficient cross-modal …

The problem of cross-modal retrieval, eg, using a text query to search for images and vice-
versa, is considered in this paper. A novel model involving correspondence autoencoder …

In order to exploit the abundant potential information of the unlabeled data and contribute to
analyzing the correlation among heterogeneous data, we propose the semi-supervised …

Cross-modal retrieval has become an active study field with the expanding scale of
multimodal data. To date, most existing methods transform multimodal data into a common …

Cross-modal retrieval aims to retrieve the pertinent samples across different modalities,
which is important in numerous multimodal applications. It is challenging to correlate the …

Cross-modal retrieval emphasizes understanding inter-modality semantic correlations,
which is often achieved by designing a similarity function. Generally, one of the most …

Cross-modal retrieval has recently attracted growing attention, which aims to match
instances captured from different modalities. The performance of cross-modal retrieval …

Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval
becomes popular with a burst of 2D and 3D data. However, this problem is challenging …

In this work, we address the problem of cross-modal retrieval in presence of multi-label
annotations. In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA) …