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 …

Multimodal learning is a framework for building models that make predictions based on
different types of modalities. Important challenges in multimodal learning are the inference of …

Recently, GAN has successfully contributed to making single-image super-resolution (SISR)
methods produce more realistic images. However, natural images have complex distribution …

Due to the fast inference and good performance, discriminative learning methods have been
widely studied in image denoising. However, these methods mostly learn a specific model …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …

Network embedding is an important method to learn low-dimensional representations of
vertexes in networks, aiming to capture and preserve the network structure. Almost all the …

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 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 …

With the explosive growth of multimedia contents, multimedia retrieval is facing
unprecedented challenges on both storage cost and retrieval speed. Hashing technique can …

Hashing has been widely investigated for large-scale image retrieval due to its search
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …