A survey for image resizing

Lin, Xiao; Ma, Yinglan; Ma, Lizhuang; Zhang, Ruiling

doi:10.1631/jzus.c1400102

Cited by 23 publications

(15 citation statements)

References 58 publications

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“…As an immediate question driven by Theorem 1.2 , one can consider the following asymptotic behavior: as δ tends to zero, where denotes a distribution, ( ), and is defined as follows: We have known that when , the sub-fBm coincides with a standard Brownian motion B . So, the two results above are some natural extensions to Brownian motion (see, for example, Csörgö and Révész [ 9 ], Dudley and Norvaivsa [ 7 ], Lin et al [ 10 ]). This paper is organized as follows.…”

Section: Introduction and Main Resultsmentioning

confidence: 99%

“…On the other hand, since Chung’s law and Strassen’s functional law of the iterated logarithm appeared, the functional law of the iterated logarithm and its rates for some classes of Gaussian processes have been discussed by many authors (see, for example, Csörgö and Révész [ 9 ], Lin et al [ 10 ], Dudley and Norvaiša [ 7 ], Malyarenko [ 11 ]). However, almost all results considered only some Gaussian processes with stationary increments, and there has been little systematic investigation on other self-similar Gaussian processes (see, for example, Norvaiša [ 8 ], Tudor and Xiao [ 12 ], and Yan et al [ 13 ]).…”

Section: Introduction and Main Resultsmentioning

confidence: 99%

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A law of iterated logarithm for the subfractional Brownian motion and an application

Yan

2018

J Inequal Appl

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Section: Introduction and Main Resultsmentioning

confidence: 99%

Section: Introduction and Main Resultsmentioning

confidence: 99%

A law of iterated logarithm for the subfractional Brownian motion and an application

Yan

2018

J Inequal Appl

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“…• Resizing-the process of scaling a video's frames from a given height and width (h, w) to a new height and width (h , w ) via spatial up-sampling or downsampling [135]. Ratio jittering [136] is resizing that permutes the aspect ratio done for data diversification.…”

Section: ) Geometric Augmentation Methodsmentioning

confidence: 99%

Video Action Understanding

Hutchinson¹,

Gadepally²

2021

IEEE Access

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Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in this emerging and rapidly evolving field. With a pedagogical emphasis, this tutorial introduces and systematizes fundamental topics, basic concepts, and notable examples in supervised video action understanding. Specifically, we clarify a taxonomy of action problems, catalog and highlight video datasets, describe common video data preparation methods, present the building blocks of state-of-the-art deep learning model architectures, and formalize domain-specific metrics to baseline proposed solutions. This tutorial is intended to be accessible to a general computer science audience and assumes a conceptual understanding of supervised learning.

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“…[85] Lin et al compared three MOFs with different pore sizes and pore structures: ZIF-8, MOF-5, and UiO-66 for their ability to adsorb lithium polysulfides. [86] These MOFs were grown in situ on the glass fiber (GF) membrane. The surface of the GF membrane modified by MOFs was coated with a layer of Ketjen black (KB) as the LSB separator and then assembled into cells for electrochemical tests.…”

Section: Original Mofsmentioning

confidence: 99%

Multifunctional MOF‐Based Separator Materials for Advanced Lithium–Sulfur Batteries

Yuan

Sun

Yang

et al. 2021

Adv Materials Inter

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Despite the high theoretical specific capacity of 1675 mAh g−1, lithium–sulfur batteries (LSBs) are still far away from wide commercialization due to the poor sulfur/Li2S electroconductivity and the polysulfides shuttle effect. In order to alleviate the active materials shuttling, separators in LSBs are required to guarantee the fast lithium‐ion transfer as well as the strong polysulfides immobilization. Therefore, various functional materials have been employed to modify the separator to achieve this goal. Among them, metal–organic frameworks (MOFs) with easy morphology design and high ionic or electronic conductivity have been widely investigated. This review summarizes the recent advances of MOF‐based interlayer for LSBs separator functionalization. Original MOFs, MOF derivatives, and MOF composites are included in this discussion. The mechanism of the enhancement of the electrochemical performance of each modified separator is explicated. Furthermore, the prospect of this promising area is provided.

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A survey for image resizing

Cited by 23 publications

References 58 publications

A law of iterated logarithm for the subfractional Brownian motion and an application

A law of iterated logarithm for the subfractional Brownian motion and an application

Video Action Understanding

Multifunctional MOF‐Based Separator Materials for Advanced Lithium–Sulfur Batteries

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