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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 more components than the image counterparts as they need to exploit the 超越K線戰法和斐波那契技術 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 超越K線戰法和斐波那契技術 restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis 超越K線戰法和斐波那契技術 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.超越K線戰法和斐波那契技術 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 超越K線戰法和斐波那契技術 2021 11:23:38 UTC (12,105 KB)
Accessible arXiv
Do you navigate 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 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 as they need to exploit the additional temporal dimension. Complex designs are 超越K線戰法和斐波那契技術 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 超越K線戰法和斐波那契技術 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 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,超越K線戰法和斐波那契技術 104 KB)
[v2] Wed, 7 Apr 2021 11:23:38 UTC (12,105 KB)
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Proteomics beyond trypsin
Peptide-centered shotgun analysis of proteins has been the core technology in 超越K線戰法和斐波那契技術 mass spectrometry based proteomics and has enabled numerous biological discoveries, such as 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 conversion of proteins into peptides in these so-called bottom-up approaches is nearly uniquely done by using trypsin as a proteolytic reagent. Here, we argue that our view of the proteome still remains incomplete and this is partially due to the nearly exclusive use of trypsin. Newly emerging alternative proteases and/or multi-protease protein digestion aim to increase proteome sequence coverage and improve 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 the identification of post-translational modifications, through the analysis of complementary and often 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 超越K線戰法和斐波那契技術 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; mass spectrometry; middle-down proteomics; proteases; protein post-translational modifications; shotgun proteomics.
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