Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of
multiple data modalities has the potential to reshape various applications. This paper …

The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

Cross-modal retrieval aims to enable flexible retrieval across different modalities. The core
of cross-modal retrieval is how to measure the content similarity between different types of …

In recent years, deep neural networks have emerged as a dominant machine learning tool
for a wide variety of application domains. However, training a deep neural network requires …

Thanks to the success of deep learning, cross-modal retrieval has made significant progress
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …

Cross-modal hashing encodes the multimedia data into a common binary hash space in
which the correlations among the samples from different modalities can be effectively …

Due to its computation efficiency and retrieval quality, hashing has been widely applied to
approximate nearest neighbor search for large-scale image retrieval, while deep hashing …

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been
widely used for similarity search in multimedia retrieval applications. However, most existing …

For many computer vision applications such as image captioning, visual question
answering, and person search, learning discriminative feature representations at both image …