In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …

Recently, the transform-based tensor nuclear norm (TNN) methods have shown promising
performance and drawn increasing attention in tensor completion (TC) problems. The main …

The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction.
Tensor low-rank methods preserve the inherent high-dimensional structure of data, allowing …

Tensor learning is a powerful tool for big data analytics and machine learning, eg, gene
analysis and deep learning. However, tensor learning algorithms are compute-intensive …

Large-scale deep neural networks (DNNs) have achieved impressive success in many
applications. However, two challenges often arise in DNN deployment in Internet of Things …

Current video generation models usually convert signals indicating appearance and motion
received from inputs (eg, image, text) or latent spaces (eg, noise vectors) into consecutive …

Video snapshot compressive imaging (SCI) samples 3D high-speed video frames with
temporally varying spatial modulation and compresses them into a single 2D measurement …

Snapshot compressive imaging (SCI) cameras compress high-speed videos or
hyperspectral images into measurement frames. However, decoding the data frames from …

It is important to estimate the global network traffic data from partial traffic measurements for
many network management tasks, including status monitoring and fault detection. However …

Tensor decompositions that factorize multidimensional data into latent factors have become
a powerful tool for big data analytics and machine learning, eg, video processing, deep …