Complete defeat of Japanese IT cloud industry forces university students to cope with the situation to a different direction: to be enrolled in an application service provider which uses US clouds such as AWS and Azure. The author argues the reason to have become a loser was that Japanese computer engineers could not persuade their superiors who graduated from authentic electric/electronic departments. They usually look down computer engineers in terms of basic math. and physics ability, but do not know what consistent-hashing technology is, for example. On the other hand, computer engineers in oldest stratum aged around sixty-years old at most, assumingly could not cause their bosses (management) to launch new cloud project …
Complete defeat of Japanese IT cloud industry forces university students to cope with the situation to a different direction: to be enrolled in an application service provider which uses US clouds such as AWS and Azure. The author argues the reason to have become a loser was that Japanese computer engineers could not persuade their superiors who graduated from authentic electric/electronic departments. They usually look down computer engineers in terms of basic math. and physics ability, but do not know what consistent-hashing technology is, for example. On the other hand, computer engineers in oldest stratum aged around sixty-years old at most, assumingly could not cause their bosses (management) to launch new cloud project …
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
An opening talk at ICDAR2017 Future Workshop - Beyond 100%Seiichi Uchida
What are the possible future research directions for OCR researchers (when we achieve almost 100% accuracy)? This slide is for a short opening talk to stimulate audiences. Actually, young researchers on OCR or other document processing-related research need to think about their "NEXT".
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ディープニューラルネットワークによるフィルタリング:
白黒画像のカラー化
Iizuka+, “Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification”. TOG2016]九州大学 数理・データサイエンス教育研究センター/ 年 月版2018 4