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pro vyhledávání: '"Image transformations"'
Autor:
Lee, Seungeun, Oh, Seungsang
Recent advancements in deep learning for tabular data have demonstrated promising performance, yet interpretable models remain limited, with many relying on complex and large-scale architectures. This paper introduces Table2Image, an interpretable fr
Externí odkaz:
http://arxiv.org/abs/2412.06265
Autor:
Lindeberg, Tony
When observing the surface patterns of objects delimited by smooth surfaces, the projections of the surface patterns to the image domain will be subject to substantial variabilities, as induced by variabilities in the geometric viewing conditions, an
Externí odkaz:
http://arxiv.org/abs/2411.05673
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs. Despite their impressive capabilities, they often produce "hallucinatory" outputs that do n
Externí odkaz:
http://arxiv.org/abs/2405.17821
Autor:
Spanos, Nikolaos, Arsenos, Anastasios, Theofilou, Paraskevi-Antonia, Tzouveli, Paraskevi, Voulodimos, Athanasios, Kollias, Stefanos
The absence of well-structured large datasets in medical computer vision results in decreased performance of automated systems and, especially, of deep learning models. Domain generalization techniques aim to approach unknown domains from a single da
Externí odkaz:
http://arxiv.org/abs/2406.00298
Autor:
Shifman, Ofir, Weiss, Yair
Deep neural networks that achieve remarkable performance in image classification have previously been shown to be easily fooled by tiny transformations such as a one pixel translation of the input image. In order to address this problem, two approach
Externí odkaz:
http://arxiv.org/abs/2404.07153
Autor:
Bose, Laurie, Dudek, Piotr
Pixel Processor Arrays (PPA) present a new vision sensor/processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication. Such a device allows visual data to be effic
Externí odkaz:
http://arxiv.org/abs/2403.16994
Autor:
Lindeberg, Tony
The influence of natural image transformations on receptive field responses is crucial for modelling visual operations in computer vision and biological vision. In this regard, covariance properties with respect to geometric image transformations in
Externí odkaz:
http://arxiv.org/abs/2311.10543
Akademický článek
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Publikováno v:
IEEE Access, Vol 12, Pp 186217-186228 (2024)
Deep learning has revolutionized image recognition. One significant obstacle still remains, the vulnerability of these models to adversarial attacks. These attacks manipulate images with subtle changes that cause CNN misclassification. While methods,
Externí odkaz:
https://doaj.org/article/3e3a28104c0241a1af7e3077aaeb309e
Adversarial attacks can affect the object recognition capabilities of machines in wild. These can often result from spurious correlations between input and class labels, and are prone to memorization in large networks. While networks are expected to
Externí odkaz:
http://arxiv.org/abs/2310.07725