Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

Event cameras sense the intensity changes asynchronously and produce event streams with
high dynamic range and low latency. This has inspired research endeavors utilizing events …

Succinct representation of complex signals using coordinate-based neural representations
(CNRs) has seen great progress, and several recent efforts focus on extending them for …

Learning-based video compression is currently a popular research topic, offering the
potential to compete with conventional standard video codecs. In this context, Implicit Neural …

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal
redundancies in videos. Index-based INRs ignore the content-specific spatial features and …

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …

Neural fields, also known as coordinate-based or implicit neural representations, have
shown a remarkable capability of representing, generating, and manipulating various forms …

We introduce SUPREYES–a novel self-supervised method to increase the spatio-temporal
resolution of gaze data recorded using low (er)-resolution eye trackers. Despite continuing …

To apply optical flow in practice, it is often necessary to resize the input to smaller
dimensions in order to reduce computational costs. However, downsizing inputs makes the …

This work addresses continuous space-time video super-resolution (C-STVSR) that aims to
up-scale an input video both spatially and temporally by any scaling factors. One key …