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text-align: center;>div idgpt_pc_blog_header>/div>/div>div classpost>div classbg-bor-inner>div classbg-bor-top>div classbg-bor-bottom>div classttl-post>div classinner>p classpost-date> 2019年 07月 22日 /p>h2>span classico> a hrefhttps://dldl.exblog.jp/239386114/>Faster R-CNN/a> /span>/h2>/div>/div>div classpost-cont>p classadminmenu-body> /p> !-- interest_match_relevant_zone_start --> div>br>/div>div>h1 classtitle mathjax>Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks/h1>div>https://arxiv.org/abs/1506.01497/div>div>br>/div>div>2015年発表の物体検出DL。/div>div>従来のR-CNNに比べ超高速高精度担っている。/div>div>これの発展形には、Mask R-CNN(矩形だけでなく、セグメンテーションもできる物体認識)がある。/div>div>h1>R-CNNとは、「物体がありそうなところ」を見当つけておいてdetectionのCNNにかけることで、CNNの実行回数を減らして高速化しようという取り組み。/h1>div>h1>Faster R-CNNは、「物体がありそうなところ」を見当つけるのもCNNでやっちゃおうというもの。/h1>div>h1>「物体がありそうなところ」を見当つけるNWは、RPN (Region Proposal Network)と呼ばれる。/h1>div>RPNは、画像全体からCNNで抽出した特徴マップに対して、候補領域の矩形と、その領域の物体らしさ(Objectness)を表すスコアを出力する。/div>div>br>/div>/div>div>br>/div>/div>/div>/div>div>br>/div>div>h1 classentry-title itempropheadline>Faster R-CNN：ディープラーニングによる一般物体検出手法/h1>/div>div>https://blog.negativemind.com/2019/02/20/general-object-recognition-faster-r-cnn//div>div>br>/div>div>h1 classit-Header_title itempropheadline>最新のRegion CNN(R-CNN)を用いた物体検出入門 ~物体検出とは? R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN~/h1>/div>div>https://qiita.com/arutema47/items/8ff629a1516f7fd485f9/div>div>h1 classit-Header_title itempropheadline>物体検出についての歴史まとめ/h1>div>https://qiita.com/mshinoda88/items/9770ee671ea27f2c81a9br>/div>/div> br classclear/> !-- interest_match_relevant_zone_end --> div classbbs_preview> /div> span idcmt239386114 data-ex-idform-239386114>/span> /div>div classpost-tail> a hrefhttps://dldl.exblog.jp/239386114/>#/a> span classTIME> by span classAUTHOR>dl-dl/span> | a hrefhttps://dldl.exblog.jp/239386114/>2019-07-22 00:35/a> /span>div idhx-comment-target-239386114>/div> /div>/div>/div>/div>/div>div classpost>div classbg-bor-inner>div classbg-bor-top>div classbg-bor-bottom>div classttl-post>div classinner>p classpost-date> 2019年 07月 21日 /p>h2>span classico> a hrefhttps://dldl.exblog.jp/239385888/>Deeplabv3+(なか)/a> /span>/h2>/div>/div>div classpost-cont>p classadminmenu-body> /p> !-- interest_match_relevant_zone_start --> div>br>/div>div>Object Detection with Deep Learning: A Review/div>div>https://arxiv.org/pdf/1807.05511.pdf/div>div>br>/div>div>●Deeplabv3+（2019/07/21現在Semantic SegmentationのSOTA）関連/div>div>Improving Semantic Segmentation via Video Propagation and Label Relaxation/div>div>https://arxiv.org/pdf/1812.01593v3.pdf/div>div>br>/div>div>Encoder-Decoder with Atrous SeparableConvolution for Semantic Image Segmentation/div>div>https://arxiv.org/pdf/1802.02611.pdf/div>div>以下、意訳br>/div>div>アブスト/div>div>エンコーダーデコーダーNWと、空間ピラミッドプーリングの両方の利点（デコーダー部で鮮明な境界が得られる、複数解像度での走査でマルチスケールなコンテキストを符号化できる）を取り入れた「span classgmail-tlid-translation gmail-translation langja>DeepLabv3 +/span>」というNWを作った。/div>div>span classgmail-tlid-translation gmail-translation langja>DeepLabv3/span>の拡張系であり、明瞭な境界を与えるためのデコーダーを追加した。/div>div>さらにspan classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>Depthwise separable/span>/span>畳み込みを空間プラミッド、デコーダーの両方に適用した。これにより高速化と高精度化を達成した。br>/div>div>br>/div>div>１ イントロbr>/div>div>span classgmail-tlid-translation gmail-translation langja>複数のスケールでコンテキスト情報を捉えるために、DeepLabv3は、異なる倍率で複数並列のatrous畳み込み（Atrous Spatial Pyramid Pooling、ASPP）を適用する。/span>/div>div>span classgmail-tlid-translation gmail-translation langja>豊富な情報は取得できるが、pooling、ストライドのために明瞭な境界は作れない。/span>/div>div>span classgmail-tlid-translation gmail-translation langja>畳み込みを緻密に行うことで改善可能だが、計算が重い。(今のNWの層数、出力のmapの解像度的に)/span>/div>div>span classgmail-tlid-translation gmail-translation langja>そこで、エンコーダーデコーダーを用いる。/span>/div>div>span classgmail-tlid-translation gmail-translation langja>エンコーダは計算資源に応じて密度を調節可能である。また、デコーダーは李密な境界を復元可能である。/span>/div>div>span classgmail-tlid-translation gmail-translation langja>（めんどいので中略）br>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>要は、/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・強力なエンコーダモジュールとしてDeepLabv3を使用し、シンプルで効果的なデコーダモジュールを使用する新しいエンコーダ - デコーダ構造を提案します。 br>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・Xceptionモデルをセグメンテーションタスクに適応させ、ASPPモジュールとデコーダモジュールの両方にspan classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>Depthwise separable/span>/span>畳み込みを適用することで、より高速でより強力なエンコーダ - デコーダネットワークを実現します。 br>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・ PASCALVOC 2012 と Cityscapes datasetsにおいてSOTA/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・TF実装はここにある（https://github.com/tensorflow/models/tree/master/research/deeplab）/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>br>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>２ Related Work/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>Spatial pyramid pooling、Encoder-decoder、Depthwise separable convolutionの説明/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>Spatial pyramid pooling/span>/span> br>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>br>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>br>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・Encoder-decoder/span>/span>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>特徴マップを徐々に減少させ、より高い意味情報を捕捉するエンコーダモジュールと、空間情報を徐々に回復するデコーダモジュール で構成される。br>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>br>/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>・Depthwise separable convolution/span>/span>/span>/span>/div>div>span classgmail-tlid-translation gmail-translation langja>span classgmail-tlid-translation gmail-translation langja>計算コストおよびパラメータの数を削減する強力な操作。/span>/span>/div>div>br>/div>div>３ Methods/div>div>・Atrous convolution/div>div>＝dilation conv/div>div>(rate＝１のときは普通のconv、２のときはbayerのRだけにフィルタかけるのと同じ)/div>div>br>/div>div>・Depthwise separable convolution/div>div>画像参照/div>div>br>/div>div>br>/div>div>br>/div>div>br>/div>div>br>/div>div>br>/div>div>Keras implementation of Deeplab v3+ with pretrained weights /div>div>https://github.com/bonlime/keras-deeplab-v3-plus/div>div>br>/div>div>DeepLab v3+（意訳）/div>div>https://qiita.com/mine820/items/14e7c556b358dbc4ee9a/div>div>br>/div>div>論文解説 Depthwise Separable Convolution for Neural Machine Translation (SliceNet)/div>div>（Depthwise separable convolutionの解説乗ってる）br>/div>div>http://deeplearning.hatenablog.com/entry/slicenet/div>div>br>/div>div>Kerasの作者@fcholletさんのCVPR17論文XceptionとGoogleのMobileNets論文を読んだ/div>div>(Xceptionとかdepthwise convの解説ある)br>/div>div>https://qiita.com/yu4u/items/34cd33b944d8bdca142d/div> br classclear/> !-- interest_match_relevant_zone_end --> div classbbs_preview> /div> span idcmt239385888 data-ex-idform-239385888>/span> /div>div classpost-tail> a hrefhttps://dldl.exblog.jp/239385888/>#/a> span classTIME> by span classAUTHOR>dl-dl/span> | a hrefhttps://dldl.exblog.jp/239385888/>2019-07-21 22:22/a> /span>div idhx-comment-target-239385888>/div> /div>/div>/div>/div>/div>div classpost>div classbg-bor-inner>div classbg-bor-top>div classbg-bor-bottom>div classttl-post>div classinner>p classpost-date> 2019年 07月 21日 /p>h2>span classico> a hrefhttps://dldl.exblog.jp/239385884/>DL系リンク集(検索用:Deeplearning、リンク、まとめ、サイト、ツール、arXiv)/a> /span>/h2>/div>/div>div classpost-cont>p classadminmenu-body> /p> !-- interest_match_relevant_zone_start --> div>●まとめ系/div>div>日本人運営のarXiv(DL系)まとめ/div>div>https://github.com/arXivTimes/arXivTimes/div>div>各分野のSOTAの手法がまとまっているサイトbr>/div>div>https://paperswithcode.com/sota/div>div>各分野のSOTAの手法がまとまっているサイト(更新頻度？)/div>div>https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems/div>div>TwitterでバズってるarXivまとめ/div>div>http://www.arxiv-sanity.com/toptwtr/div>div>br>/div>div>●物体検出の歴史まとめbr>/div>div>https://qiita.com/mshinoda88/items/9770ee671ea27f2c81a9br>/div>div>br>/div>div>●勉強向けbr>/div>div>https://deeplearning.jp//div>div>br>/div>div>●論文役立ちツールbr>/div>div>http://dream-exp.net/shaper//div>div>https://translate.google.com/?hlja&tabTT&authuser0/div> br classclear/> !-- interest_match_relevant_zone_end --> div classbbs_preview> /div> span idcmt239385884 data-ex-idform-239385884>/span> /div>div classpost-tail> a hrefhttps://dldl.exblog.jp/239385884/>#/a> span classTIME> by span classAUTHOR>dl-dl/span> | a hrefhttps://dldl.exblog.jp/239385884/>2019-07-21 22:17/a> /span>div 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