In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms
in video inpainting (VI). Despite the effectiveness of these components, they still suffer from …

This paper reviews the state of the art in visual privacy protection techniques, with particular
attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A …

Low-light images suffer from low contrast and unclear details, which not only reduces the
available information for humans but limits the application of computer vision algorithms …

High-quality video inpainting that completes missing regions in video frames is a promising
yet challenging task. State-of-the-art approaches adopt attention models to complete a frame …

Transformer, as a strong and flexible architecture for modelling long-range relations, has
been widely explored in vision tasks. However, when used in video inpainting that requires …

We propose a flow-guided transformer, which innovatively leverage the motion discrepancy
exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video …

Differently from 2D image datasets such as COCO, largescale human datasets with 3D
ground-truth annotations are very difficult to obtain in the wild. In this paper, we address this …

Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

Recently there has been an interest in the potential of learning generative models from a
single image, as opposed to from a large dataset. This task is of significance, as it means …