In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of
multimodal data. It takes one type of data as the query to retrieve relevant data of another …

Cross-modal retrieval aims to enable flexible retrieval experience across different modalities
(eg, texts vs. images). The core of cross-modal retrieval research is to learn a common …

Matching images and sentences demands a fine understanding of both modalities. In this
article, we propose a new system to discriminatively embed the image and text to a shared …

Given a query instance from one modality (eg, image), cross-modal retrieval aims to find
semantically similar instances from another modality (eg, text). To perform cross-modal …

Heterogeneous face recognition (HFR) aims at matching facial images acquired from
different sensing modalities with mission-critical applications in forensics, security and …

In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …

Most existing zero-shot learning approaches exploit transfer learning via an intermediate
semantic representation shared between an annotated auxiliary dataset and a target dataset …

In this paper, we propose a novel deep generative approach to cross-modal retrieval to
learn hash functions in the absence of paired training samples through the cycle consistency …

In many computer vision systems, the same object can be observed at varying viewpoints or
even by different sensors, which brings in the challenging demand for recognizing objects …

Binary feature descriptors such as local binary patterns (LBP) and its variations have been
widely used in many face recognition systems due to their excellent robustness and strong …