Zobrazeno 1 - 10
of 700
pro vyhledávání: '"A. Monsefi"'
Publikováno v:
Tulīd va Farāvarī-i Maḥṣūlāt-i Zirā̒ī va Bāghī, Vol 13, Iss 1, Pp 15-27 (2023)
In order to investigate the effect of pre-emergence and post-emergence herbicides on nutrient depletion, weed population and nutrient uptake by wheat in sesame-wheat cropping system in Khuzestan a field experiment was conducted in randomized complete
Externí odkaz:
https://doaj.org/article/77078240b6ee4c5b8b09b759675329ae
Autor:
Navard, Pouyan, Monsefi, Amin Karimi, Zhou, Mengxi, Chao, Wei-Lun, Yilmaz, Alper, Ramnath, Rajiv
Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. Methods like ControlNet and T2I-Adapter excel at following sketches by
Externí odkaz:
http://arxiv.org/abs/2410.01595
Autor:
Monsefi, Amin Karimi, Zhou, Mengxi, Monsefi, Nastaran Karimi, Lim, Ser-Nam, Chao, Wei-Lun, Ramnath, Rajiv
We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-defined frequencies in the input image and employs a reconstruction loss
Externí odkaz:
http://arxiv.org/abs/2409.10362
In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models, particularly CLIP, in handling detail-oriented and fine-grained tasks like segmentation. While CLIP and it
Externí odkaz:
http://arxiv.org/abs/2409.06809
Autor:
Monsefi, Amin Karimi, Shiri, Pouya, Mohammadshirazi, Ahmad, Monsefi, Nastaran Karimi, Davies, Ron, Moosavi, Sobhan, Ramnath, Rajiv
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash occurs, and informing safety policies, regulations, and targeted inte
Externí odkaz:
http://arxiv.org/abs/2402.05151
Autor:
Monsefi, Amin Karimi, Karisani, Payam, Zhou, Mengxi, Choi, Stacey, Doble, Nathan, Ji, Heng, Parthasarathy, Srinivasan, Ramnath, Rajiv
Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods
Externí odkaz:
http://arxiv.org/abs/2402.06190
Autor:
Mohammadshirazi, Ahmad, Nadafian, Aida, Monsefi, Amin Karimi, Rafiei, Mohammad H., Ramnath, Rajiv
Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quali
Externí odkaz:
http://arxiv.org/abs/2308.01438
Autor:
Mohsenipouya, Hossein1 (AUTHOR), Monsefi, Seyyed Fateme2 (AUTHOR), Hosseinnataj, Abolfazl3 (AUTHOR), Mamun, Mohammed A.4,5,6 (AUTHOR), Al-Mamun, Firoj4,5,6 (AUTHOR) firojphiju@gmail.com
Publikováno v:
BMC Research Notes. 10/7/2024, Vol. 17 Issue 1, p1-7. 7p.
Publikováno v:
in IEEE Signal Processing Letters, vol. 28, pp. 713-717, 2021
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated from the so-called proposal distribution, and the choice of this prop
Externí odkaz:
http://arxiv.org/abs/2209.13716
Road construction projects maintain transportation infrastructures. These projects range from the short-term (e.g., resurfacing or fixing potholes) to the long-term (e.g., adding a shoulder or building a bridge). Deciding what the next construction p
Externí odkaz:
http://arxiv.org/abs/2209.06813