The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

State-of-the-art methods in the image-to-image translation are capable of learning a
mapping from a source domain to a target domain with unpaired image data. Though the …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

We propose a novel model named Multi-Channel Attention Selection Generative Adversarial
Network (SelectionGAN) for guided image-to-image translation, where we translate an input …

Multi-modal fusion is proven to be an effective method to improve the accuracy and
robustness of speaker tracking, especially in complex scenarios. However, how to combine …

We investigate exocentric-to-egocentric cross-view translation, which aims to generate a first-
person (egocentric) view of an actor based on a video recording that captures the actor from …

It is hard to generate an image at target view well for previous cross-view image translation
methods that directly adopt a simple encoder-decoder or U-Net structure, especially for …

Unsupervised image-to-image translation has received considerable attention due to the
recent remarkable advancements in generative adversarial networks (GANs). In image-to …