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最近の物体検出 2019/05/30

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最近の物体検出 2019/05/30

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最近の物体検出 2019/05/30

  1. 1. 最近の物体検出 2019/05/30
  2. 2. お品書き •イントロ • 歴史の振り返り •最近の動向 • キーポイント系 • multi-scale対応
  3. 3. イントロ
  4. 4. https://twitter.com/RUSH1L/status/889963452143357952/photo/1https://www.youtube.com/watch?v=VOC3huqHrss
  5. 5. MegDet 詳細不明 (ensemble) 52.5 SNIP (R-FCN, DPN-98, DCN) (ensemble) 48.3 Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 SNIPER (Faster R-CNN, R-101, DCN) NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-101 NAS-FPN RetinaNet R-50 M2Det VGG-16 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN
  6. 6. MegDet 詳細不明 (ensemble) 52.5 SNIP (R-FCN, DPN-98, DCN) (ensemble) 48.3 Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 SNIPER (Faster R-CNN, R-101, DCN) NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-101 NAS-FPN RetinaNet R-50 M2Det VGG-16 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN
  7. 7. まずは歴史の復習 2012以降の流れ
  8. 8. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 年表作った SqueezeNet
  9. 9. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 後半の話 SqueezeNet
  10. 10. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 識別 SqueezeNet
  11. 11. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 検出 SqueezeNet
  12. 12. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 基本backboneが強ければ強い (augmentation系は微妙) SqueezeNet モデルを強くしたい
  13. 13. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt モデルを軽くしたい 基本backboneが速ければ速い SqueezeNet
  14. 14. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 検出 SqueezeNet
  15. 15. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage 1-stage SqueezeNet
  16. 16. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage SqueezeNet物体っぽいところを1つ1つ識別 (遅めだが精度高い傾向)
  17. 17. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 1-stage SqueezeNet基本, sliding window (FCN) (精度低めだが速い傾向)
  18. 18. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage https://arxiv.org/abs/1311.2524 CVPR 2014 TPAMI 2015
  19. 19. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage https://arxiv.org/abs/1504.08083 CVPR 2015
  20. 20. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage https://arxiv.org/abs/1506.01497 NIPS 2015
  21. 21. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 1-stage https://arxiv.org/abs/1506.02640 CVPR 2016 YOLOv1はちょっと例外
  22. 22. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt multi-scale対応 (主に)小さい物体に強くする
  23. 23. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt multi-scale対応 https://arxiv.org/abs/1612.03144 CVPR 2017
  24. 24. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt とりあえず入れて おく系 SqueezeNet
  25. 25. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt とりあえず入れて おく系 https://arxiv.org/abs/1703.06211 CVPR 2017
  26. 26. SqueezeNet 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt とりあえず入れて おく系 https://arxiv.org/abs/1704.04503 ICCV 2017
  27. 27. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage SqueezeNet
  28. 28. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage SqueezeNet
  29. 29. 2012 2013 2014 2015 2016 2017 2018 2019 AlexNet VGG GoogLeNet ResNet DenseNet SE-Net R-CNN OverFeat Fast R-CNN Faster R-CNN SSD YOLOv2 YOLOv3 Mask R-CNN CornerNetRetinaNet Deformable Conv Soft-NMS MobileNetv2 MobileNetv3 AmoebaNet MobileNet NASNet NAS-FPNFPN YOLO NAS ResNeXt 2-stage SqueezeNet
  30. 30. 最近のdetection
  31. 31. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet
  32. 32. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース CornerNetとその派生
  33. 33. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet multi-scale対応
  34. 34. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet BN周り バッチサイズ小さいと BNで悪影響出る問題
  35. 35. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet BN周り SyncBN 使ってバッチサイズあ げるといいよ https://arxiv.org/abs/1711.07240 CVPR 2018
  36. 36. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet BN周り https://arxiv.org/abs/1803.08494 ECCV 2018
  37. 37. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet BN周り https://arxiv.org/abs/1903.10520
  38. 38. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet pretrain関係 pretrainそもそも必要? いいpretrainとは?
  39. 39. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet pretrain関係 https://arxiv.org/abs/1811.08883
  40. 40. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet pretrain関係 https://arxiv.org/abs/1811.12231 ICLR 2019
  41. 41. 最近のdetection キーポイントベース手法
  42. 42. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  43. 43. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  44. 44. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 物体の左上コーナーを heatmap で予測
  45. 45. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 物体の右下コーナーを heatmap で予測
  46. 46. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 左上コーナーと右下コーナーを対 応づけるembeddingも学習
  47. 47. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 コーナー点は基 本物体の外
  48. 48. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 Corner Pooling
  49. 49. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 •1-stageの割に精度高い (COCO AP 40.6) • ただし, horizontal flip の TTA… •遅い (4fps) •学習が死ぬほど遅い (10 GPU で 2 週間)
  50. 50. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 •1-stageの割に精度高い (COCO AP 40.6) • ただし, horizontal flip の TTA… •遅い (4fps) •学習が死ぬほど遅い (10 GPU で 2 週間) AWS (オンデマンド)だっ たら100万超える
  51. 51. CornerNet: Detecting Objects as Paired Keypoints https://arxiv.org/abs/1808.01244 ECCV 2018 CornerNet Hourglass-104
  52. 52. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  53. 53. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 コーナー点, 物体の外だし見 つけるの難しくない?
  54. 54. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 コーナー点, 物体の外だし見 つけるの難しくない? スクショ撮るときとか位置ズ レたりするよね
  55. 55. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 コーナー点, 物体の外だし見 つけるの難しくない? 人間でもコーナーじゃなくて, 物体の上下左右 の端出す方が楽って話もあるなぁ Extreme clicking for efficient object annotation (ICCV 2017) https://arxiv.org/abs/1708.02750 スクショ撮るときとか位置ズ レたりするよね
  56. 56. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 物体の上下 左右の端の heatmap
  57. 57. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 中心も予測
  58. 58. Bottom-up Object Detection by Grouping Extreme and Center Points https://arxiv.org/abs/1901.08043 CVPR 2019 位置関係でグルー ピングできる
  59. 59. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  60. 60. CenterNet: Keypoint Triplets for Object Detection https://arxiv.org/abs/1904.08189 コーナーとかエッジだけ見て物体の クラス当てるの難しくない?
  61. 61. CenterNet: Keypoint Triplets for Object Detection https://arxiv.org/abs/1904.08189 コーナーとかエッジだけ見て物体の クラス当てるの難しくない? 普通に考えてbboxの中見た 方が性能上がりそう
  62. 62. CenterNet: Keypoint Triplets for Object Detection https://arxiv.org/abs/1904.08189 コーナーに加えて中心も予測 位置関係でグルーピングできる
  63. 63. CenterNet: Keypoint Triplets for Object Detection https://arxiv.org/abs/1904.08189 bboxの中も見る (Cascaded Corner Pooling) bboxの中も見る (Center Pooling)
  64. 64. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  65. 65. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 やっぱCornerNet遅いよね
  66. 66. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 やっぱCornerNet遅いよね backbone軽くするか, 入力小 さくしたらよくない?
  67. 67. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 CornerNet (multi-scale)
  68. 68. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 CornerNet (single-scale)
  69. 69. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 CornerNet (single-scale)
  70. 70. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 SqueezeNet & MobileNet の アイディアでbackboneを軽く した
  71. 71. CornerNet-Lite: Efficient Keypoint Based Object Detection https://arxiv.org/abs/1904.08900 縮小して推論し, それっぽいと ころをcropして再度推論
  72. 72. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  73. 73. Objects as Points https://arxiv.org/abs/1904.07850 CornerNetとかあるけど, 普通 に中心じゃダメなの?
  74. 74. Objects as Points https://arxiv.org/abs/1904.07850 CornerNetとかあるけど, 普通 に中心じゃダメなの? 素朴にやっても結構いけそう
  75. 75. Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 CenterNet Hourglass-104 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN
  76. 76. Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN CenterNet Hourglass-104
  77. 77. Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN CenterNet Hourglass-104
  78. 78. Mask R-CNN ResNeXt-152 32x8d CornerNet Hourglass-104 NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN CenterNet Hourglass-104
  79. 79. Objects as Points https://arxiv.org/abs/1904.07850 物体中心を heatmapで予測
  80. 80. Objects as Points https://arxiv.org/abs/1904.07850 downsampleして る分のoffset補正
  81. 81. Objects as Points https://arxiv.org/abs/1904.07850 サイズは直接 回帰
  82. 82. Objects as Points https://arxiv.org/abs/1904.07850 高解像度のfeature mapを使う bboxレベルでの NMSはしない
  83. 83. Objects as Points https://arxiv.org/abs/1904.07850 3D物体検出に も適用可
  84. 84. Objects as Points https://arxiv.org/abs/1904.07850 キーポイント検出に も適用可
  85. 85. Objects as Points 直接回帰すると精 度はイマイチ https://arxiv.org/abs/1904.07850
  86. 86. Objects as Points heatmapで予測して offset補正もする https://arxiv.org/abs/1904.07850
  87. 87. Objects as Points 補正 https://arxiv.org/abs/1904.07850
  88. 88. Objects as Points グルーピングと 見てもok https://arxiv.org/abs/1904.07850
  89. 89. Objects as Points https://arxiv.org/abs/1904.07850 •シンプルかつ汎用性が高い •速くて強い •フェアな比較か? • deformable conv… •ablationしてない •学習は遅め • ResNet-101版 & DLA-34 版は 8 GPU で 2.5 日 • Hourglass-104 版は 5 GPU で 5 日
  90. 90. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet キーポイントベース
  91. 91. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 2-stage モデルでもheatmap 使ってみたくない?
  92. 92. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 2-stage モデルでもheatmap 使ってみたくない? でもやっぱコーナーとか微妙 だよね。物体の外だし
  93. 93. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 2-stage モデルでもheatmap 使ってみたくない? でもやっぱコーナーとか微妙 だよね。物体の外だし グリッドにして情報伝播させ たら?
  94. 94. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 ベースは普通の 2-stageモデル
  95. 95. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 回帰部分をheatmap に置き換え
  96. 96. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 隣接グリッド点から情 報を伝播
  97. 97. Grid R-CNN https://arxiv.org/abs/1811.12030 CVPR 2019 proposalから物体がは み出してたとき対策
  98. 98. 最近のdetection multi-scale対応
  99. 99. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet multi-scale対応
  100. 100. An Analysis of Scale Invariance in Object Detection - SNIP https://arxiv.org/abs/1711.08189 CVPR 2018 multi-scale対応ってimage pyramidとか FPN とかやり方色々あるけど, 結局どう するのがいいの?
  101. 101. An Analysis of Scale Invariance in Object Detection - SNIP https://arxiv.org/abs/1711.08189 CVPR 2018 multi-scale対応ってimage pyramidとか FPN とかやり方色々あるけど, 結局どう するのがいいの? 試したら?
  102. 102. An Analysis of Scale Invariance in Object Detection - SNIP https://arxiv.org/abs/1711.08189 CVPR 2018 検出タスクでは 物体サイズの違 いがやばい
  103. 103. An Analysis of Scale Invariance in Object Detection - SNIP https://arxiv.org/abs/1711.08189 CVPR 2018 image pyramid + single-scale モデルが良さそう
  104. 104. An Analysis of Scale Invariance in Object Detection - SNIP https://arxiv.org/abs/1711.08189 CVPR 2018 image pyramid でバリエーションを増や しつつ, single-scaleモデルを学習
  105. 105. SNIP (R-FCN, DPN-98, DCN) (ensemble) 48.3 CornerNet Hourglass-104 SNIPER (Faster R-CNN, R-101, DCN) NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-101 NAS-FPN RetinaNet R-50 M2Det VGG-16 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN SNIP (single model, flip) 45.7 Mask R-CNN ResNeXt-152 32x8d
  106. 106. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet multi-scale対応
  107. 107. SNIPER: Efficient Multi-Scale Training https://arxiv.org/abs/1805.09300 NeurIPS 2018 SNIP 強いけど, image pyramid 全体使っ てるから学習効率悪いよね
  108. 108. SNIPER: Efficient Multi-Scale Training https://arxiv.org/abs/1805.09300 NeurIPS 2018 SNIP 強いけど, image pyramid 全体使っ てるから学習効率悪いよね image pyramid からcropした画像で学習 したらよくない?
  109. 109. SNIPER: Efficient Multi-Scale Training https://arxiv.org/abs/1805.09300 NeurIPS 2018 SNIP 強いけど, image pyramid 全体使っ てるから学習効率悪いよね image pyramid からcropした画像で学習 したらよくない? crop使うとバッチサイズも上げられて一 石二鳥じゃん
  110. 110. SNIPER: Efficient Multi-Scale Training https://arxiv.org/abs/1805.09300 NeurIPS 2018
  111. 111. SNIP (R-FCN, DPN-98, DCN) (ensemble) 48.3 CornerNet Hourglass-104 SNIPER (Faster R-CNN, R-101, DCN) NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-101 NAS-FPN RetinaNet R-50 M2Det VGG-16 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN SNIP (single model, flip) 45.7 Mask R-CNN ResNeXt-152 32x8d
  112. 112. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet multi-scale対応
  113. 113. AutoFocus: Efficient Multi-Scale Inference https://arxiv.org/abs/1812.01600 SNIPER で学習は速くなったけど, 結局推 論はimage pyramid全体でやるから遅い よね
  114. 114. AutoFocus: Efficient Multi-Scale Inference https://arxiv.org/abs/1812.01600 SNIPER で学習は速くなったけど, 結局推 論はimage pyramid全体でやるから遅い よね 小さい物体がありそうなとこだけ拡大 して再度推論するようにしたらよくな い?
  115. 115. AutoFocus: Efficient Multi-Scale Inference https://arxiv.org/abs/1812.01600 普通に検出 入力
  116. 116. AutoFocus: Efficient Multi-Scale Inference https://arxiv.org/abs/1812.01600 小さい物体がありそ うな領域を予測
  117. 117. AutoFocus: Efficient Multi-Scale Inference https://arxiv.org/abs/1812.01600 小さい物体がありそうな領域をcrop して再度入力 (繰り返し)
  118. 118. SNIP (R-FCN, DPN-98, DCN) (ensemble) 48.3 CornerNet Hourglass-104 SNIPER (Faster R-CNN, R-101, DCN) NAS-FPN RetinaNet, AmoebaNet YOLOv3 DarkNet53 RetinaNet R-101 NAS-FPN RetinaNet R-50 M2Det VGG-16 RetinaNet R-50 CenterNet DLA-34, DCN CenterNet R-18, DCN SNIP (single model, flip) 45.7 Mask R-CNN ResNeXt-152 32x8d AutoFocus (RetinaNet, R-101-FPN) 47.9 (6.4fps)
  119. 119. 2018/01 3 5 7 9 108 64211 12 SNIP SNIPER IoU-Net CornerNet GroupNorm KL Loss YOLOv3 Cascade R-CNN 2019/01 3 5 64211 12 AutoFocus ExtremeNet TridentNet CenterNet CornerNet-lite CenterNet RepPoints Grid R-CNN NAS-FPNFSAF M2Det WSrethinking texture DCNv2 MegDet multi-scale対応
  120. 120. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection https://arxiv.org/abs/1904.07392 最近NAS流行ってるけど, 物体検出にも 使えないの? CVPR 2019
  121. 121. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection https://arxiv.org/abs/1904.07392 最近NAS流行ってるけど, 物体検出にも 使えないの? そういえばFPNの構造シンプルだよね CVPR 2019
  122. 122. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection https://arxiv.org/abs/1904.07392 最近NAS流行ってるけど, 物体検出にも 使えないの? そういえばFPNの構造シンプルだよね 色んなレベルの特徴混ぜ合わせたら強 くなりそう CVPR 2019
  123. 123. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection https://arxiv.org/abs/1904.07392 最近NAS流行ってるけど, 物体検出にも 使えないの? そういえばFPNの構造シンプルだよね 色んなレベルの特徴混ぜ合わせたら強 くなりそう FPN繰り返すのもありなんじゃない? CVPR 2019
  124. 124. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection https://arxiv.org/abs/1904.07392 CVPR 2019
  125. 125. まとめ •キーポイント系のdetectionがブーム •multi-scale学習の工夫でかなり強くできる •NASがdetectionにもやってきた

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