Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

We present ControlNet, a neural network architecture to add spatial conditioning controls to
large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large …

We argue that the theory and practice of diffusion-based generative models are currently
unnecessarily convoluted and seek to remedy the situation by presenting a design space …

Neural graphics primitives, parameterized by fully connected neural networks, can be costly
to train and evaluate. We reduce this cost with a versatile new input encoding that permits …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …

Abstract Low-light Image Enhancement (LIE) aims at improving contrast and restoring
details for images captured in low-light conditions. Most of the previous LIE algorithms adjust …

We address the problem of estimating a high quality dense depth map from a single RGB
input image. We start out with a baseline encoder-decoder convolutional neural network …

The goal when imaging bioprocesses with optical microscopy is to acquire the most
spatiotemporal information with the least invasiveness. Deep neural networks have …