Zobrazeno 1 - 10
of 441
pro vyhledávání: '"I.4.10"'
Deep neural networks can obtain impressive performance on various tasks under the assumption that their training domain is identical to their target domain. Performance can drop dramatically when this assumption does not hold. One explanation for thi
Externí odkaz:
http://arxiv.org/abs/2410.06349
Autor:
Valouev, Aleksey
Point spread function (PSF) engineering is vital for precisely controlling the focus of light in computational imaging, with applications in neural imaging, fluorescence microscopy, and biophotonics. The PSF is derived from the magnitude of the Fouri
Externí odkaz:
http://arxiv.org/abs/2410.05413
Autor:
Kashyap, Pankhi, Tandon, Pavni, Gupta, Sunny, Tiwari, Abhishek, Kulkarni, Ritwik, Jadhav, Kshitij Sharad
Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss funct
Externí odkaz:
http://arxiv.org/abs/2410.04084
Federated Learning (FL) facilitates data privacy by enabling collaborative in-situ training across decentralized clients. Despite its inherent advantages, FL faces significant challenges of performance and convergence when dealing with data that is n
Externí odkaz:
http://arxiv.org/abs/2410.03499
We propose two new evaluation metrics to assess realness of generated images based on normalizing flows: a simpler and efficient flow-based likelihood distance (FLD) and a more exact dual-flow based likelihood distance (D-FLD). Because normalizing fl
Externí odkaz:
http://arxiv.org/abs/2410.02004
Autor:
Zheng, Xinyuan, Ravid, Orren, Barry, Robert A. J., Kim, Yoojean, Wang, Qian, Kim, Young-geun, Zhu, Xi, He, Xiaofu
Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. The complexity of ASD has resulted in a deficiency of objective diagnostic biomarkers. Deep learning methods have g
Externí odkaz:
http://arxiv.org/abs/2410.00068
Learning a discriminative model to distinguish a target from its surrounding distractors is essential to generic visual object tracking. Dynamic target representation adaptation against distractors is challenging due to the limited discriminative cap
Externí odkaz:
http://arxiv.org/abs/2409.18901
Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe art
Externí odkaz:
http://arxiv.org/abs/2409.15731
Current video summarization methods largely rely on transformer-based architectures, which, due to their quadratic complexity, require substantial computational resources. In this work, we address these inefficiencies by enhancing the Direct-to-Summa
Externí odkaz:
http://arxiv.org/abs/2409.14724
Autor:
Foussereau, Virgile, Dumas, Robin
Achieving human-like memory recall in artificial systems remains a challenging frontier in computer vision. Humans demonstrate remarkable ability to recall images after a single exposure, even after being shown thousands of images. However, this capa
Externí odkaz:
http://arxiv.org/abs/2409.11750