Zobrazeno 1 - 10
of 77 868
pro vyhledávání: '"Mehta, P. P."'
Dependable visual drone detection is crucial for the secure integration of drones into the airspace. However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shif
Externí odkaz:
http://arxiv.org/abs/2412.04789
Autor:
Morishita, Takahiro, Mason, Charlotte A., Kreilgaard, Kimi C., Trenti, Michele, Treu, Tommaso, Vulcani, Benedetta, Zhang, Yechi, Abdurro'uf, Alavi, Anahita, Atek, Hakim, Bahe, Yannick, Bradac, Marusa, Bradley, Larry D., Bunker, Andrew J., Coe, Dan, Colbert, James, Gelli, Viola, Hayes, Matthew J., Jones, Tucker, Kodama, Tadayuki, Leethochawalit, Nicha, Liu, Zhaoran, Malkan, Matthew A., Mehta, Vihang, Metha, Benjamin, Newman, Andrew B., Rafelski, Marc, Roberts-Borsani, Guido, Rutkowski, Michael J., Scarlata, Claudia, Stiavelli, Massimo, Sutanto, Ryo A., Takahashi, Kosuke, Teplitz, Harry I., Wang, Xin
We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\si
Externí odkaz:
http://arxiv.org/abs/2412.04211
Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across musical genres are heavily influenced by the availability of training dat
Externí odkaz:
http://arxiv.org/abs/2412.04100
Autor:
Tank, Chayan, Mehta, Shaina, Pol, Sarthak, Katoch, Vinayak, Anand, Avinash, Jaiswal, Raj, Shah, Rajiv Ratn
In recent times, more and more people are posting about their mental states across various social media platforms. Leveraging this data, AI-based systems can be developed that help in assessing the mental health of individuals, such as suicide risk.
Externí odkaz:
http://arxiv.org/abs/2412.01353
Autor:
Lai, Bolin, Juefei-Xu, Felix, Liu, Miao, Dai, Xiaoliang, Mehta, Nikhil, Zhu, Chenguang, Huang, Zeyi, Rehg, James M., Lee, Sangmin, Zhang, Ning, Xiao, Tong
Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been applied for instructions that are underrepresented in the training set, or
Externí odkaz:
http://arxiv.org/abs/2412.01027
Autor:
Livanos, Vasilis, Mehta, Ruta
The I.I.D. Prophet Inequality is a fundamental problem where, given $n$ independent random variables $X_1,\dots,X_n$ drawn from a known distribution $\mathcal{D}$, one has to decide at every step $i$ whether to stop and accept $X_i$ or discard it for
Externí odkaz:
http://arxiv.org/abs/2411.19851
Autor:
Zamfir, Eduard, Wu, Zongwei, Mehta, Nancy, Tan, Yuedong, Paudel, Danda Pani, Zhang, Yulun, Timofte, Radu
Recent advancements in all-in-one image restoration models have revolutionized the ability to address diverse degradations through a unified framework. However, parameters tied to specific tasks often remain inactive for other tasks, making mixture-o
Externí odkaz:
http://arxiv.org/abs/2411.18466
Autor:
Shandilya, Anurag, Bhat, Swapnil, Gautam, Akshat, Yadav, Subhash, Bhatt, Siddharth, Mehta, Deval, Jadhav, Kshitij
Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. Fo
Externí odkaz:
http://arxiv.org/abs/2411.17535
Autor:
Mehta, Vedant
Neurological and Physiological Disorders that impact emotional regulation each have their own unique characteristics which are important to understand in order to create a generalized solution to all of them. The purpose of this experiment is to expl
Externí odkaz:
http://arxiv.org/abs/2411.14666
Accurate segmentation of Optical Coherence Tomography (OCT) images is crucial for diagnosing and monitoring retinal diseases. However, the labor-intensive nature of pixel-level annotation limits the scalability of supervised learning with large datas
Externí odkaz:
http://arxiv.org/abs/2411.12615