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論文輪読: Instance-sensitive Fully Convolutional Networks
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Instance-sensitive Fully Convolutional Networks http://arxiv.org/abs/1603.08678
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Social Media Marketing Trends 2024 // The Global Indie Insights
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論文輪読: Instance-sensitive Fully Convolutional Networks
1.
論文輪読: Instance-sensi,ve Fully Convolu,onal Networks Jifeng Dai , Kaiming He , Yi Li , Shaoqing Ren , Jian Sun hFp://arxiv.org/abs/1603.08678
2.
この論文について • What – FCNを用いたInstance proposal手法の提案 –
おそらくFCNによるinstance proposalは初 • How – Instance-sensi,ve score map with FCN • Contribu,on – Fast & Accurate proposals • Experiment – PASCAL VOC 2012 & MS COCO segmenta,on – state of the art results
3.
MS COCO val (赤線+白で塗りつぶしされている領域はmissed ground truth)
4.
書誌情報 • Title: Instance-sensi,ve Fully Convolu,onal Networks • Author: Jifeng Dai , Kaiming He , Yi Li , Shaoqing Ren , Jian Sun –
MSRA, 清華大学, 中国科学技術大学 • hFp://arxiv.org/abs/1603.08678 (29 Mar 2016, Technical Report)
5.
書誌情報 • 著者の最近の研究 今回紹介する手法は Proposalに関しては これより概ね性能が良い
6.
(余談) CVPR’16 面白そうなの • CNN-RNN: A Unified Framework for Mul,-label Image Classifica,on – hFps://arxiv.org/abs/1604.04573 •
Mul,-Oriented Text Detec,on with Fully Convolu,onal Networks – hFps://arxiv.org/abs/1604.04018 • ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks – hFps://arxiv.org/abs/1511.03776 FCN 利用
7.
Classifica,on Detec,on Seman,c Segmenta,on Instance Object Proposals AlexNet VGG GoogLeNet 2012 2013 2014 2015 2016 OverFeat R-CNN Fast R-CNN Faster R-CNN FCN FCN+CRF (DeepLab etc..) DeepMask CaffeNet Residual (BatchNormaliza,on) Object Proposals (Selec,ve Search) 2011 (BING) (GOP) (MCG) (MCG) (EdgeBoxes) (RIGOR) (DPM) InstanceFCN [今回紹介する手法] ※独断と偏見で作成(適当) 空白 ≠ 研究がない
8.
Classifica,on vs Detec,on • Detec,on = What + Where Classifica,on Detec,on Person Motorbike Person Motorbike
9.
Seman,c Segmenta,on vs Instance Segment Proposals • Seman,c Segmenta,on = Pixel-wise labeling • Instance Segmenta,on = インスタンスを区別する •
Instance Segment Proposals : 物体領域候補を抽出 – 各領域が何であるかを識別するのは別の問題 – 基本的にRecallの方が重要 Original Seman,c Segmenta,on Instance Segmenta,on
10.
Fully Convolu,onal Networks (FCN) • No fully connect layer • Last layer: per-pixel sommax •
Seman,c Segmenta,onでは本質的に重なる同一物体を区別 できない
11.
FCNの応用 • Seman,c Segmenta,on • Contour Detec,on •
Denoising • Image Super Resolu,on • Image Enhancement • Region Proposal Network (RPN) – Faster R-CNN 基本的にはピクセルごとの分類問題に適用できる
12.
DeepMask (NIPS 2015) • Facebook AI Research • CNNに入力した画像の中心のピクセルが属している 物体部分のマスクだけ出力するように学習
13.
提案手法: InstanceFCN • FCNをinstance segmenta,onするように拡張
14.
from FCN to InstanceFCN • インスタンスが1つ => FCNでOK • インスタンスが2つ以上の場合: –
左のインスタンスから見ると,右のインスタンスが区別で きれば良い
15.
InstanceFCN • FCNの出力として各ピクセルは相対位置にあるインスタンス に自分自身も含まれるかどうかを出力する – 出力マップ数はk^2 •
最終的出力: m × m のスライディングウィンドウに対してm/k × m/k のサブ ウィンドウごとに出力マップをコピー
16.
DeepMaskとの比較 DeepMaskはm^2の層から マスクを生成する InstanceFCNの場合 全結合層がなく FCNからマスクを生成する
17.
InstanceFCNの利点 • 各ピクセルでのスコアは再利用できる
18.
ネットワーク構造の詳細 - VGG16ベース - 2つの分岐 - セグメンテーション - インスタンススコア - インスタンススコアを計算したのち,ウィン ドウごとにassemblingをおこなう
19.
訓練 / テスト 方法 • 訓練時: スライディングウィンドウを適当に動かして,各スラ イディングウィンドウごとに評価 –
損失関数 – SGDで訓練 • テスト時: 端から端までスライディングウィンドウを動かして, 各評価を合計する – 1枚あたり約1.5s - pi* = 1 (posi,ve sampleの場合) - Si* : ground truth segment instance - L: logis,c regression
20.
実験: 定性的結果 k=3
21.
実験: DeepMaskとの比較 • DeepMaskよりも良い精度が得られている – AR@N: Average Recall, Nはproposalの数 –
~DeepMaskは著者らによる実装 – crop 224x224というのは訓練時の画像サイズをDeepMask と同じにしたもの PASCAL VOC 2012 val kを変えた場合: k=5
22.
実験: 他のInstance Segment Proposals手法との比較 PASCAL VOC 2012 val MNCも同一著者 によるもの (CVPR’16)
23.
実験: 他のInstance Segment Proposals手法との比較 MS COCOC val
24.
実験: Seman,c Instance Segmenta,on • proposal部分にInstanceFCNを使用 PASCAL VOC 2012 val
25.
Result DeepMaskとの比較
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