Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Graikos A"'
Autor:
Belagali, Varun, Yellapragada, Srikar, Graikos, Alexandros, Kapse, Saarthak, Li, Zilinghan, Nandi, Tarak Nath, Madduri, Ravi K, Prasanna, Prateek, Saltz, Joel, Samaras, Dimitris
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 of the same image. To perform this view-invariance task, current SSL
Externí odkaz:
http://arxiv.org/abs/2412.01672
Autor:
Yellapragada, Srikar, Graikos, Alexandros, Triaridis, Kostas, Prasanna, Prateek, Gupta, Rajarsi R., Saltz, Joel, Samaras, Dimitris
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Given that it is infeasible to directly train a model on 'whole' images
Externí odkaz:
http://arxiv.org/abs/2411.16969
Diffusion models have dominated the field of large, generative image models, with the prime examples of Stable Diffusion and DALL-E 3 being widely adopted. These models have been trained to perform text-conditioned generation on vast numbers of image
Externí odkaz:
http://arxiv.org/abs/2410.18804
Autor:
Le, Minh-Quan, Graikos, Alexandros, Yellapragada, Srikar, Gupta, Rajarsi, Saltz, Joel, Samaras, Dimitris
Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image domains such as digital histopathology and remote sensing. Existing m
Externí odkaz:
http://arxiv.org/abs/2407.14709
Autor:
Miao, Qiaomu, Graikos, Alexandros, Zhang, Jingwei, Mondal, Sounak, Hoai, Minh, Samaras, Dimitris
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. We propose the first semi-supervised method for gaze following by intr
Externí odkaz:
http://arxiv.org/abs/2406.02774
Autor:
Graikos, Alexandros, Yellapragada, Srikar, Le, Minh-Quan, Kapse, Saarthak, Prasanna, Prateek, Saltz, Joel, Samaras, Dimitris
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-level annotation effort required in specialized domains like histopath
Externí odkaz:
http://arxiv.org/abs/2312.07330
Autor:
Yellapragada, Srikar, Graikos, Alexandros, Prasanna, Prateek, Kurc, Tahsin, Saltz, Joel, Samaras, Dimitris
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 pathology. Conditioning on labeled data is known to help in data-efficient m
Externí odkaz:
http://arxiv.org/abs/2309.00748
Denoising diffusion models have gained popularity as a generative modeling technique for producing high-quality and diverse images. Applying these models to downstream tasks requires conditioning, which can take the form of text, class labels, or oth
Externí odkaz:
http://arxiv.org/abs/2306.01900
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, output quality drops significantly due to the shape-radiance ambiguity
Externí odkaz:
http://arxiv.org/abs/2303.17712
Autor:
Hu, Edward J., Malkin, Nikolay, Jain, Moksh, Everett, Katie, Graikos, Alexandros, Bengio, Yoshua
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 latents. A key tradeoff in modeling the posteriors over latents is betw
Externí odkaz:
http://arxiv.org/abs/2302.06576