most citedWhere Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

6 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations

Yi Zhang, Chun-Wun Cheng, Junyi He +4

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simpl…

cs.CV20246 cited

Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

Amer Essakine, Yanqi Cheng, Chun-Wun Cheng +5

Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applicatio…

cs.CV2024

NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning

Yi Zhang, Chun-Wun Cheng, Ke Yu +3

In this paper, we consider the problem of prototype-based vision-language reasoning problem. We observe that existing methods encounter three major challenges: 1) escalating resour…

cs.LG2024

Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis

Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng +2

Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-s…

cs.CV20233 cited

Continuous U-Net: Faster, Greater and Noiseless

Chun-Wun Cheng, Christina Runkel, Lihao Liu +3

Image segmentation is a fundamental task in image analysis and clinical practice. The current state-of-the-art techniques are based on U-shape type encoder-decoder networks with sk…