20 citations · 32 across the 18 of their papers we have counts for
21 papers
BigCell: Generating Gigapixel Whole-Slide Images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10
Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models…
On the spectral properties of generative denoiser Jacobians
Alexandros Graikos, Nebojsa Jojic, Dimitris Samaras
Generative denoising models, such as diffusion and flow-matching, learn to sample from complex distributions by training a deep neural network denoiser to recover clean data from n…
Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
Irene Kim, Sai Tanmay Reddy Chakkera, Alexandros Graikos +2
Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encode…
Generating metamers of human scene understanding
Ritik Raina, Abe Leite, Alexandros Graikos +3
Human vision combines low-resolution "gist" information from the visual periphery with sparse but high-resolution information from fixated locations to construct a coherent underst…
GriDiT: Factorized Grid-Based Diffusion for Efficient Long Image Sequence Generation
Snehal Singh Tomar, Alexandros Graikos, Arjun Krishna +2
Modern deep learning methods typically treat image sequences as large tensors of sequentially stacked frames. However, is this straightforward representation ideal given the curren…
Mitigating Diffusion Model Hallucinations with Dynamic Guidance
Kostas Triaridis, Alexandros Graikos, Aggelina Chatziagapi +2
Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to…
PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +11
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have bee…
PathSegDiff: Pathology Segmentation using Diffusion model representations
Sachin Kumar Danisetty, Alexandros Graikos, Srikar Yellapragada +1
Image segmentation is crucial in many computational pathology pipelines, including accurate disease diagnosis, subtyping, outcome, and survivability prediction. The common approach…
Pathology Image Compression with Pre-trained Autoencoders
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +6
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compre…
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali, Srikar Yellapragada, Alexandros Graikos +7
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views…
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +4
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Gi…
Fast constrained sampling in pre-trained diffusion models
Alexandros Graikos, Nebojsa Jojic, Dimitris Samaras
Large denoising diffusion models, such as Stable Diffusion, have been trained on billions of image-caption pairs to perform text-conditioned image generation. As a byproduct of thi…
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite Dimensions
Minh-Quan Le, Alexandros Graikos, Srikar Yellapragada +3
Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image…
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following
Qiaomu Miao, Alexandros Graikos, Jingwei Zhang +3
Training gaze following models requires a large number of images with gaze target coordinates annotated by human annotators, which is a laborious and inherently ambiguous process.…
Learned representation-guided diffusion models for large-image generation
Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le +4
To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure the painstaking patch-…
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna +3
To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathol…
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras
Denoising diffusion models have gained popularity as a generative modeling technique for producing high-quality and diverse images. Applying these models to downstream tasks requir…
S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces
Haoyu Wu, Alexandros Graikos, Dimitris Samaras
Neural rendering of implicit surfaces performs well in 3D vision applications. However, it requires dense input views as supervision. When only sparse input images are available, o…
GFlowNet-EM for learning compositional latent variable models
Edward J. Hu, Nikolay Malkin, Moksh Jain +3
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the l…
Diffusion models as plug-and-play priors
Alexandros Graikos, Esmeralda S. Whitammer, Nebojsa Jojic +1
We consider the problem of inferring high-dimensional data in a model that consists of a prior and an auxiliary differentiable constraint $c(\mathbf{x}…
Resolving label uncertainty with implicit posterior models
Esther Rolf, Nikolay Malkin, Alexandros Graikos +3
We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly…