Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Mohan, Sreyas"'
Autor:
Kotovenko, Dmytro, Grebenkova, Olga, Sarafianos, Nikolaos, Paliwal, Avinash, Ma, Pingchuan, Poursaeed, Omid, Mohan, Sreyas, Fan, Yuchen, Li, Yilei, Ranjan, Rakesh, Ommer, Björn
While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches demonstrate proficiency in transferring colors and textures but often st
Externí odkaz:
http://arxiv.org/abs/2409.17917
Autor:
Crozier, Peter A., Leibovich, Matan, Haluai, Piyush, Tan, Mai, Thomas, Andrew M., Vincent, Joshua, Mohan, Sreyas, Morales, Adria Marcos, Kulkarni, Shreyas A., Matteson, David S., Wang, Yifan, Fernandez-Granda, Carlos
Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of n
Externí odkaz:
http://arxiv.org/abs/2407.17669
Autor:
Wang, Peihao, Fan, Zhiwen, Xu, Dejia, Wang, Dilin, Mohan, Sreyas, Iandola, Forrest, Ranjan, Rakesh, Li, Yilei, Liu, Qiang, Wang, Zhangyang, Chandra, Vikas
Score distillation has emerged as one of the most prevalent approaches for text-to-3D asset synthesis. Essentially, score distillation updates 3D parameters by lifting and back-propagating scores averaged over different views. In this paper, we revea
Externí odkaz:
http://arxiv.org/abs/2401.00604
Autor:
Wang, Peihao, Xu, Dejia, Fan, Zhiwen, Wang, Dilin, Mohan, Sreyas, Iandola, Forrest, Ranjan, Rakesh, Li, Yilei, Liu, Qiang, Wang, Zhangyang, Chandra, Vikas
Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as "Janus" artifact, where the generated objects fake each view with multiple front faces
Externí odkaz:
http://arxiv.org/abs/2401.00909
Autor:
Gong, Xinyu, Mohan, Sreyas, Dhingra, Naina, Bazin, Jean-Charles, Li, Yilei, Wang, Zhangyang, Ranjan, Rakesh
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We tho
Externí odkaz:
http://arxiv.org/abs/2305.07214
Autor:
Marcos-Morales, Adria, Leibovich, Matan, Mohan, Sreyas, Vincent, Joshua Lawrence, Haluai, Piyush, Tan, Mai, Crozier, Peter, Fernandez-Granda, Carlos
Unsupervised denoising is a crucial challenge in real-world imaging applications. Unsupervised deep-learning methods have demonstrated impressive performance on benchmarks based on synthetic noise. However, no metrics are available to evaluate these
Externí odkaz:
http://arxiv.org/abs/2210.05553
Autor:
Liu, Sheng, Kaku, Aakash, Zhu, Weicheng, Leibovich, Matan, Mohan, Sreyas, Yu, Boyang, Huang, Haoxiang, Zanna, Laure, Razavian, Narges, Niles-Weed, Jonathan, Fernandez-Granda, Carlos
Publikováno v:
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781, 2022
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a
Externí odkaz:
http://arxiv.org/abs/2111.10734
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we obser
Externí odkaz:
http://arxiv.org/abs/2109.05771
Autor:
Mohan, Sreyas, Vincent, Joshua L., Manzorro, Ramon, Crozier, Peter A., Simoncelli, Eero P., Fernandez-Granda, Carlos
Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing when applied to data that deviate from the training distri
Externí odkaz:
http://arxiv.org/abs/2107.12815
Autor:
Vincent, Joshua L., Manzorro, Ramon, Mohan, Sreyas, Tang, Binh, Sheth, Dev Y., Simoncelli, Eero P., Matteson, David S., Fernandez-Granda, Carlos, Crozier, Peter A.
A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The
Externí odkaz:
http://arxiv.org/abs/2101.07770