Due to the rapid generation and dissemination of information, large language models
(LLMs) quickly run out of date despite enormous development costs. To address the crucial …

Generalizable implicit neural representation (INR) enables a single continuous function, ie,
a coordinate-based neural network, to represent multiple data instances by modulating its …

This work introduces reduced models based on Continuous Low Rank Adaptation
(CoLoRA) that pre-train neural networks for a given partial differential equation and then …

Motion modeling is critical in flow-based Video Frame Interpolation (VFI). Existing paradigms
either consider linear combinations of bidirectional flows or directly predict bilateral flows for …

Abstract Implicit Neural Representation (INR) has gained increasing popularity as a data
representation method, serving as a prerequisite for innovative generation models. Unlike …

Recently, implicit neural representation (INR) has been applied to image compression.
However, the rate-distortion performance of most existing INR-based image compression …

Urban time series, such as mobility flows, energy consumption, and pollution records,
encapsulate complex urban dynamics and structures. However, data collection in each city …

Despite the abundant availability and content richness for video data, its high-dimensionality
poses challenges for video research. Recent advancements have explored the implicit …

In this paper, we propose a neural representation for videos that enables real-time quality-
scalable decoding, called QS-NeRV. QS-NeRV comprises a Self-Learning Distribution …

Abstract Implicit Neural Networks (INRs) have emerged as powerful representations to
encode all forms of data, including images, videos, audios, and scenes. With video, many …