Xiaolong Wang
Sihyun Yu
Max Ehrlich
Sihyun Yu
Max Ehrlich
Description: Implicit Neural Representation for Vision
machine learning (3763) deep learning (1245) computer vision (849) university of maryland (30) inr (26) implicit neural representation (1) neural fields (1)
An emerging area within deep learning, implicit neural representation (INR), also known as neural fields, offers a powerful new mechanism and paradigm for processing and representing visual data.In contrast with the dominant big data setting, INR focuses on neural networks which parameterize a field, often in a coordinate-based manner. The most well-known of this class of models is NeRF, which has been wildly successful for 3D modeling, especially for novel view synthesis. INR for 2D images and videos have
This is a relatively new area in vision, with many opportunities to propose new algorithms, extend existing applications, and innovate entirely new systems. Since working with INRs often requires less resources than many areas, this sort of research is especially accessible in the academic setting. Additionally, while there are many workshops for NeRF, there are often none for the incredibly broad spectrum of other INR work. Therefore, we propose this workshop as an avenue to build up the fledgling INR comm
The simple design and flexibility offered by INRs, and recent work that proposes hypernetworks to circumvent expensive per-model training, points to the potential of INR a unified architecture that can efficiently represent audio, images, video and 3D data. The following are some of the key directions and challenges in INR.
Xiaolong Wang
Sihyun Yu
Max Ehrlich
Sihyun Yu
Max Ehrlich