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超越K線戰法和斐波那契技術

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Computer Science > Computer Vision and Pattern Recognition

Title: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Abstract: Video super-resolution (VSR) approaches tend to have 超越K線戰法和斐波那契技術 more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We 超越K線戰法和斐波那契技術 further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

Comments: CVPR 2021 camera-ready
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2012.02181 [cs.CV]
(or arXiv:2012.02181v2 [cs.CV] for this version)超越K線戰法和斐波那契技術
https://doi.org/10.48550/arXiv.2012.02181

Focus to learn more

Submission history

From: Kelvin C.K. Chan [view email]
[v1] Thu, 3 Dec 2020 18:56:14 UTC (12,104 KB)
[v2] Wed, 7 Apr 2021 11:23:38 UTC (12,105 KB)

Accessible arXiv

Do you navigate 超越K線戰法和斐波那契技術 超越K線戰法和斐波那契技術 arXiv using a screen reader or other assistive technology? Are you a professor who helps students do so? We want to hear from you. Please 超越K線戰法和斐波那契技術 consider signing up to share your insights as we work to make 超越K線戰法和斐波那契技術 arXiv even more open.

Computer Science > Computer Vision and Pattern Recognition

Title: 超越K線戰法和斐波那契技術 BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Abstract: Video super-resolution (VSR) approaches tend to have more components than the image counterparts 超越K線戰法和斐波那契技術 as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing 超越K線戰法和斐波那契技術 improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

Comments: CVPR 2021 camera-ready
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2012.02181 [cs.CV]
(or arXiv:2012.02181v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2012.02181

Focus to learn more

Submission history

From: Kelvin C.K. Chan [view email]
[v1] Thu, 3 Dec 2020 18:56:14 UTC (12,104 KB)
[v2] Wed, 7 Apr 2021 11:23:38 UTC (12,超越K線戰法和斐波那契技術 105 KB)

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Proteomics beyond trypsin

Peptide-centered shotgun analysis of proteins has been the core technology in mass spectrometry based proteomics and has enabled numerous biological discoveries, such as 超越K線戰法和斐波那契技術 the large-scale charting of protein-protein interaction networks, the quantitative analysis of protein post-translational modifications and even the first drafts of the human proteome. The 超越K線戰法和斐波那契技術 conversion of proteins into peptides in these so-called bottom-up approaches is nearly 超越K線戰法和斐波那契技術 超越K線戰法和斐波那契技術 uniquely done by using trypsin as a proteolytic reagent. Here, we argue that our view of the proteome still remains incomplete and this is 超越K線戰法和斐波那契技術 partially due to the nearly exclusive use of trypsin. Newly emerging alternative 超越K線戰法和斐波那契技術 超越K線戰法和斐波那契技術 proteases and/or multi-protease protein digestion aim to increase proteome sequence coverage and improve the identification of post-translational modifications, through the analysis of complementary and often 超越K線戰法和斐波那契技術 longer peptides, introducing an approach termed middle-down proteomics. Of pivotal importance for this purpose is the identification of proteases beneficial for use in proteomics. Here, 超越K線戰法和斐波那契技術 we describe some of the shortcomings of the nearly exclusive use of trypsin in proteomics and review the properties of other proteomics-appropriate proteases. We describe favorable protease traits with an emphasis on middle-down proteomics and suggest potential sources for the discovery of new proteases. We also highlight a few examples wherein the use of other proteases than trypsin enabled the generation of more comprehensive data sets leading to previously unexplored knowledge of the proteome.

Keywords: bias in quantitative proteomics; cleavage specificity; digestion; 超越K線戰法和斐波那契技術 mass spectrometry; middle-down proteomics; proteases; protein post-translational modifications; shotgun proteomics.

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