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of 6
pro vyhledávání: '"Rawlekar, Samyak"'
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with clas
Externí odkaz:
http://arxiv.org/abs/2409.08381
Reconstructing dynamic articulated objects from a singular monocular video is challenging, requiring joint estimation of shape, motion, and camera parameters from limited views. Current methods typically demand extensive computational resources and t
Externí odkaz:
http://arxiv.org/abs/2405.12607
Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged information from vision-language models (VLMs) trained on large text-imag
Externí odkaz:
http://arxiv.org/abs/2404.16193
Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural
Externí odkaz:
http://arxiv.org/abs/2204.07314
Autor:
Wathore, Roshan, Rawlekar, Samyak, Anjum, Saima, Gupta, Ankit, Bherwani, Hemant, Labhasetwar, Nitin, Kumar, Rakesh
Publikováno v:
In Gondwana Research February 2023 114:69-77
Autor:
Huang H; Courant Institute of Mathematical Sciences, New York University, USA., Rawlekar S; Tandon School of Engineering, New York University, USA., Chopra S; Department of Radiology, New York University Langone Health, USA., Deniz CM; Department of Radiology, New York University Langone Health, USA.
Publikováno v:
Proceedings of machine learning research [Proc Mach Learn Res] 2023 Jul; Vol. 227, pp. 1385-1405.