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[DL輪読会]Objects as Points

2019/06/14 Deep Learning JP: http://deeplearning.jp/seminar-2/

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DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Objects as Points
Shizuma Kubo, Matsuo Lab
書誌情報
• 書誌情報
– Objects as Points, 通称 CenterNet
– 著者: Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
– 2019/04/16 on arXiv
• 1日違い(2019/04/17)に出たCenterNet (CenterNet: Keypoint Triplets for Object
Detection)もあるが、今回の論文とは別。
• 既存の物体認識モデルの考え方から離れ、シンプルな手法を提案。
• YOLOv3やM2Det(前回発表)より速くて精度のいいモデルもできる1stage物体認識
のモデルを達成。
2
引用: https://pjreddie.com/darknet/yolo/
はじめに
3
• シンプルな改良によって、これまで当たり前のように使われていたNMSを利
用する必要がなくなった
• 既存の物体認識は non-maxima suppression (NMS)
によって予測のダブリを取り除く必要があった。
 NMSは微分できないため学習ができず、end-to-
endの学習ができない。
 すべての候補領域について予測を出した後に
NMSを行うという処理が無駄である。
目次
4
1. 既存の物体認識との違い
2. CenterNetの推論
3. CenterNetの学習
4. CenterNetの実験
5. CenterNetの応用
6. まとめ
目次
5
1. 既存の物体認識との違い
2. CenterNetの推論
3. CenterNetの学習
4. CenterNetの実験
5. CenterNetの応用
6. まとめ
既存の物体認識との違い
• 物体認識では重複した予測結果を抑制するためにnon-maxima suppression (NMS)
という処理を行うことが一般的。
• IoU (領域の重なり度合い)を計算して、一定の閾値を超えたものを抑制する。
6
候補領域の予測 NMS
画像引用: https://meideru.com/archives/3538

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