25 papers · ranked by Valyu relevance
Shiqi Chen, Yuhang Li, Yuntian Wang, Hanlong Chen + 1 more
Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1-11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12-14. Here we present optical…
Bo Zhao, Lili Meng, Weidong Yin, Leonid Sigal
Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a…
Pei Wang, Yijun Li, Krishna Kumar Singh, Jingwan Lu + 1 more
'Nuno Vasconcelos'] We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations via matching…
Zuhao Yang, Fangneng Zhan, Kunhao Liu, Muyu Xu + 1 more
—The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual…
Estibaliz García-Huete, Sara Ignacio-Cerrato, David Pacios, José Luis Vázquez-Poletti + 5 more
This study explores the evolving role of social media in the spread of misinformation during the Ukraine-Russia conflict, with a focus on how artificial intelligence (AI) contributes to the creation of deceptive war imagery. Specifically, the research examines the relationship between color patterns (LUTs) in…
Somnuk Phon-Amnuaisuk
Affine transformation, layer blending, and artistic filters are popular processes that graphic designers employ to transform pixels of an image to create a desired effect. Here, we examine various approaches that synthesize new images: pixel-based compositing models and in particular, distributed representations of…
Wael Mattar, Idan Levy, Nir Sharon, Shai Dekel
In this paper, we take a new approach to autoregressive image generation that is based on two main ingredients. The first is wavelet image coding, which allows to tokenize the visual details of an image from coarse to fine details by ordering the information starting with the most significant bits of the most…
Samah S. Baraheem, Tam V. Nguyen, Cosimo Distante
The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This…
Rihito Tominaga, Masataka Seo, Javier Prieto, Ramón J. Durán Barroso
Image generation from natural language has become a very promising area of research on multimodal learning in recent years. In recent years, the performance of this theme has improved rapidly, and the release of powerful tools has caused a great response in various places. The Stacked Generative Adversarial Networks…
Shen‐Hsing Annabel Chen, Yuhang Li, Hanlong Chen, Aydogan Özcan
Generative models cover various application areas, including image, video and music synthesis, natural language processing, and molecular design, among many others. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge. Here, we present optical…
Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli + 6 more
'Peter Andeer' 'Peter S. Nico' 'Trent Northen' 'Daniela Ushizima' 'Raimondo Schettini' 'Guanghui (Richard) Wang'] Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive…
Benjamin L. Kidder
Deep neural networks have significantly advanced medical image analysis, yet their full potential is often limited by the relatively small dataset sizes. Generative modeling has stimulated attention for its potential applications in the synthesis of medical images. Recent advancements in diffusion models have exhibited…
Yuan Feng, Zachary Robers, Leyla Rasheed, Yang Miao + 5 more
Spatially resolved omics technologies reveal tissue organization at single-cell resolution but remain limited by the cost of the assays, incomplete spatial coverage, 2D-only imaging, and experimental artifacts. These factors motivate the need for in silico methods that can reconstruct or extend tissue context beyond…
Carlos R. Ponce, Will Xiao, Peter F. Schade, Till S. Hartmann + 2 more
Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about…
Francisco Carrillo-Perez, Marija Pizurica, Michael G. Ozawa, Hannes Vogel + 5 more
The acquisition of multi-modal biological data for the same sample, such as RNA sequencing and whole slide imaging (WSI), has increased in recent years, enabling studying human biology from multiple angles. However, despite these emerging multi-modal efforts, for the majority of studies only one modality is typically…
Jiafu Wei, Chia-Ming Chang, Xi Yang, Takeo Igarashi
In real-world usage, existing GAN image generation tools come up short due to their lack of intuitive interfaces and limited flexibility. To overcome these limitations, we developed CanvasPic, an innovative tool for flexible GAN image generation. Our tool introduces a novel 2D layout design that allows users to…
Binxu Wang, Carlos R. Ponce
Visual neurons respond across a vast landscape of images, comprising objects, textures, and places. Natural images can be parameterized using deep generative networks, raising the question of whether latent factors learned by some networks control images in ways that better align with visual neurons. We studied neurons…
Jiqing Wu, Ingrid Berg, Viktor H. Koelzer
Advanced spatial transcriptomics (ST) techniques provide comprehensive insights into complex living systems across multiple scales, while simultaneously posing challenges in bioimage analysis. The spatial co-profiling of biological tissues by gigapixel whole slide images (WSI) and gene expression arrays motivates the…
Junyu Dong, Jun Liu, Kang Yao, Mike Chantler + 3 more
'Muwei Jian'] Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture…
Johan Phan, Muhammad Sarmad, Leonardo Ruspini, Gabriel Kiss + 1 more
'Frank Lindseth'] Three-dimensional (3D) images provide a comprehensive view of material microstructures, enabling numerical simulations unachievable with two-dimensional (2D) imaging alone. However, obtaining these 3D images can be costly and constrained by resolution limitations. We introduce a novel method capable…
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…
Henning Otto Brinkhaus, Kohulan Rajan, Achim Zielesny, Christoph Steinbeck
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Kevin Kawchak
Chemical research is more effectively progressed using Large Multimodal Models (LMMs) combined with Document Retrieval and recently published literature. The methods described here illustrate significant strides over previously tested Large Language Model (LLM) multi-document workflows for characterization assistance…
Mehmet Aziz Yirik, Maria Sorokina, Christoph Steinbeck
The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generation efficiently, exhaustively and isomorphism-free, the structure generator needs to ensure the building of…