Tootfinder

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
Paul Jeanney, Ashton Hetherington, Shady E. Ahmed, David Lanceta, Susana Saiz, Jos\'e Miguel Perez, Soledad Le CLainche
https://arxiv.org/abs/2507.00539

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
This paper presents an innovative Reduced-Order Model (ROM) for merging experimental and simulation data using Data Assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach implementing the Ensemble Kalman Filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the prop…

Finetuning Stellar Spectra Foundation Models with LoRA
Xiaosheng Zhao, Yuan-Sen Ting, Alexander S. Szalay, Yang Huang
https://arxiv.org/abs/2507.20972 https://

Finetuning Stellar Spectra Foundation Models with LoRA
Foundation models are beginning to impact stellar spectroscopy, where spectra encode rich physical information in a structured, language-like form. A key challenge is adapting these models across heterogeneous surveys with differing resolution and coverage. We apply Low-Rank Adaptation (LoRA) to fine-tune SpecCLIP--a contrastively pre-trained model on LAMOST and Gaia XP spectra--for downstream tasks on DESI Early Data Release (EDR) spectra. We show that LoRA enables few-shot learning on DESI, w…

Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Zahra TehraniNasab, Amar Kumar, Tal Arbel
https://arxiv.org/abs/2508.14931

Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resolution settings. As high-resolution image synthesis becomes increasingly essential for various applications, particularly in medical imaging domains, fine-tuning emerges as a crucial mechanism for adapting these powerful pre-trained models to task-specific requirements and data distributions. In this work, we present a systematic study, examining the impact of …

Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal, Vishal Gaur, Marcus Freitag, Julian Kuehnert, Theodore van Kessel, Dinesha V. Hegde, Andr\'es Mu\~noz-Jaramillo, Johannes Jakubik, Etienne Vos, Kshitiz Mandal, Ata Akbari Asanjan, Joao Lucas de Sousa Almeida, Amy Lin, Talwinder Singh, Kang Yang, Chetraj Pandey, Jinsu Hong, Berkay Aydin, Thorsten Kurth, Ryan McGranaghan, Spiridon Kasapis, Vishal Upendran, Shah Bahauddin, Daniel da Silva, Nikolai V. Pogo…

Surya: Foundation Model for Heliophysics
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, includin…

Temporal Adaptation of Pre-trained Foundation Models for Music Structure Analysis
Yixiao Zhang, Haonan Chen, Ju-Chiang Wang, Jitong Chen
https://arxiv.org/abs/2507.13572

Temporal Adaptation of Pre-trained Foundation Models for Music Structure Analysis
Audio-based music structure analysis (MSA) is an essential task in Music Information Retrieval that remains challenging due to the complexity and variability of musical form. Recent advances highlight the potential of fine-tuning pre-trained music foundation models for MSA tasks. However, these models are typically trained with high temporal feature resolution and short audio windows, which limits their efficiency and introduces bias when applied to long-form audio. This paper presents a tempor…

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks
Xinyu Xiong, Zihuang Wu, Lei Zhang, Lei Lu, Ming Li, Guanbin Li
https://arxiv.org/abs/2508.03566

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks
Recent studies have highlighted the potential of adapting the Segment Anything Model (SAM) for various downstream tasks. However, constructing a more powerful and generalizable encoder to further enhance performance remains an open challenge. In this work, we propose SAM2-UNeXT, an advanced framework that builds upon the core principles of SAM2-UNet while extending the representational capacity of SAM2 through the integration of an auxiliary DINOv2 encoder. By incorporating a dual-resolution st…

Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Zahra TehraniNasab, Amar Kumar, Tal Arbel
https://arxiv.org/abs/2507.12698

Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts. Traditional generative architectures such as Generative Adversarial Networks (GANs) or Variational Auto Encoder (VAEs) have shown great promise for high-resolution image generation but struggle with preserving fine-grained details that are key for accurate diagnosis. To address this issue, we introduce Pixel Perfect MegaMed, the first vision-language found…

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
Changchun Yang, Haoyang Li, Yushuai Wu, Yilan Zhang, Yifeng Jiao, Yu Zhang, Rihan Huang, Yuan Cheng, Yuan Qi, Xin Guo, Xin Gao
https://arxiv.org/abs/2507.06418

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
While pathology foundation models have transformed cancer image analysis, they often lack integration with molecular data at single-cell resolution, limiting their utility for precision oncology. Here, we present PAST, a pan-cancer single-cell foundation model trained on 20 million paired histopathology images and single-cell transcriptomes spanning multiple tumor types and tissue contexts. By jointly encoding cellular morphology and gene expression, PAST learns unified cross-modal representati…

From Radar to Risk: Building a High-Resolution Hail Database for Austria And Estimating Risk Through the Integration of Distributional Neural Networks into the Metastatistical Framework
Gregor Ehrensperger, Vera Katharina Meyer, Marc-Andr\'e Falkensteiner, Tobias Hell
https://arxiv.org/abs/2507.06429

From Radar to Risk: Building a High-Resolution Hail Database for Austria And Estimating Risk Through the Integration of Distributional Neural Networks into the Metastatistical Framework
This study makes significant contributions to the understanding of hail climatology in Austria. First, it introduces a comprehensive database of hailstone sizes, constructed from three-dimensional radar data spanning 2009 to 2022 and calibrated by approximately 5000 verified hail reports. The database serves as foundation for describing the short-term climatology of hail and provides the data necessary for estimating hail risk maps with enhanced spatial resolution and quality. Second, the…

RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding
Xi Xiao, Yunbei Zhang, Janet Wang, Lin Zhao, Yuxiang Wei, Hengjia Li, Yanshu Li, Xiao Wang, Swalpa Kumar Roy, Hao Xu, Tianyang Wang
https://arxiv.org/abs/2507.17353

RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding
Accurate road damage detection is crucial for timely infrastructure maintenance and public safety, but existing vision-only datasets and models lack the rich contextual understanding that textual information can provide. To address this limitation, we introduce RoadBench, the first multimodal benchmark for comprehensive road damage understanding. This dataset pairs high resolution images of road damages with detailed textual descriptions, providing a richer context for model training. We also p…

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He, Yifan Wang, Yue Wang, Ruizhen Hu, Yiyi Liao, Xiaowei Zhou, Hujun Bao
https://arxiv.org/abs/2507.11540

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by high costs, low resolution, and environmental sensitivity, limiting their applicability in real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalizatio…

Crosslisted article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
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- Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution M...
Zahra TehraniNasab, Amar Kumar, Tal Arbel

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying
https://arxiv.org/abs/2506.11152

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Single-cell transcriptomics has become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and transcriptional regulation at the single-cell level. With the advent of spatial transcriptomics data we have the promise of learning about cells within a tissue context as it provides both spatial coordinates and transcriptomic readouts. However, existing models either ignore spatial resolution or the gene regula…

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…

Unveiling Unprecedented Fine Structure in Coronal Flare Loops with the DKIST
Cole A. Tamburri, Maria D. Kazachenko, Gianna Cauzzi, Adam F. Kowalski, Ryan French, Rahul Yadav, Caroline L Evans, Yuta Notsu, Marcel F. Corchado-Albelo, Kevin P. Reardon, Alexandra Tritschler
https://arxiv.org/abs/2508.05875…

Unveiling Unprecedented Fine Structure in Coronal Flare Loops with the DKIST
We present the highest-resolution H$α$ observations of a solar flare to date, collected during the decay phase of an X1.3-class flare on 8 August 2024 at 20:12 UT. Observations with the Visible Broadband Imager at the National Science Foundation's (NSF) Daniel K. Inouye Solar Telescope reveal dark coronal loop strands at unprecedented spatial resolution in the flare arcade above highly structured chromospheric flare ribbons. After surveying the 20 best-seeing images, we calculate a mean loop w…

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang Zhang, Laurent Dinh, Miguel Angel Bautista, Josh Susskind, Shuangfei Zhai
https://arxiv.org/abs/2506.06276

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building on this foundation, we introduce several key arch…