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Deep Learning for Vision Presented by Kevin Matzen Wednesday, - PowerPoint PPT Presentation

Sep 15, 2022 •290 likes •717 views

Deep Learning for Vision Presented by Kevin Matzen Wednesday, April 9, 14 Quick Intro - DNN Feed-forward Sparse connectivity (layer to layer) Different layer types Recently popularized for vision [Krizhevsky, et. al. NIPS 2012]

  1. Deep Learning for Vision Presented by Kevin Matzen Wednesday, April 9, 14

  2. Quick Intro - DNN • Feed-forward • Sparse connectivity (layer to layer) • Different layer types • Recently popularized for vision [Krizhevsky, et. al. NIPS 2012] Wednesday, April 9, 14

  3. The Layers • Convolution • Loss functions • Fully connected • Image processing • Pooling • Neuron activation function • Normalization Wednesday, April 9, 14

  4. deeplearning.net/tutorial/lenet.html Wednesday, April 9, 14

  5. [Krizhevsky, NIPS 2012] Wednesday, April 9, 14

  6. Software • code.google.com/p/cuda-convnet/ [nvidia gpu] • github.com/UCB-ICSI-Vision-Group/decaf-release/ [deprecated; cpu-only] • caffe.berkeleyvision.org [cpu; nvidia gpu] • research.google.com/archive/ large_deep_networks_nips2012.html [proprietary; distributed system] Wednesday, April 9, 14

  7. DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev, Christian Szegedy - CVPR 2014 DeepFace: Closing the Gap to Human-Level Performance in Face Verification Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf - CVPR 2014 Wednesday, April 9, 14

  8. DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev, Christian Szegedy - CVPR 2014 DeepFace: Closing the Gap to Human-Level Performance in Face Verification Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf - CVPR 2014 Wednesday, April 9, 14

  9. Input: Uncropped photo Output: Joint locations Wednesday, April 9, 14

  10. Pipeline 1. Person detection 2. Joint position regression 3. Joint refinement Wednesday, April 9, 14

  11. Datasets Leeds Sports Pose (LSP) [Johnson, et. al. BMVC 2010] 14 joint locations 2000 main person - 150 px Frames Labeled in Cinema (FLIC) [Sapp, et. al. CVPR 2013] 5003 person detector every 10 frames of 30 movies 20k candidates Image Parse [Ramanan NIPS 2006] mturk 305 images 10 upperbody joints similar to leeds includes casual photos Buffy Stickmen 748 frames Wednesday, April 9, 14

  12. Person Detection • Input: Uncropped image • Output: Cropped image • LSP dataset - No person detector • FLIC dataset - Enlarged face detector Wednesday, April 9, 14

  13. Wednesday, April 9, 14

  14. Main difference Wednesday, April 9, 14

  15. Wednesday, April 9, 14

  16. Runtime • 0.1s per image - 12 cores (SotA - 1.5s, 4s) • Training stage 0 - 3 days • Training refinement - 7 days each Wednesday, April 9, 14

  17. Evaluation • Percentage of Correct Parts (PCP) • Correct if predicted limb is within 1/2 of correct limb length • Percentage of Detected Joints (PDJ) • Predicted and correct joints are within some factor of torso diameter Wednesday, April 9, 14

  18. Wednesday, April 9, 14

  19. Wednesday, April 9, 14

  20. Wednesday, April 9, 14

  21. Wednesday, April 9, 14

  22. DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev, Christian Szegedy - CVPR 2014 DeepFace: Closing the Gap to Human-Level Performance in Face Verification Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf - CVPR 2014 Wednesday, April 9, 14

  23. Pipeline • Detect faces • Correct out-of-plane rotation • Generate features via CNN • Classify Wednesday, April 9, 14

  24. Alignment Wednesday, April 9, 14

  25. Fiducial Detection • LBP histograms • Support Vector Regressor • Iteratively transform and predict • 6 fiducial points for 2D alignment • 67 fiducial points for 3D alignment Wednesday, April 9, 14

  26. 3D Alignment • Iterative affine camera PnP • 3D reference - Average mesh of USF Human-ID dataset • Considers fiducial covariance • Residuals applied to reference mesh • Affine warp texture Wednesday, April 9, 14

  27. CNN Architecture Wednesday, April 9, 14

  28. CNN Architecture Features Wednesday, April 9, 14

  29. CNN Architecture weight sharing no weight sharing Wednesday, April 9, 14

  30. Training softmax cross-entropy loss -log p k Wednesday, April 9, 14

  31. Sparsity • ReLU nonlinearly - rectified linear unit max(0, x) • 75% model parameters = 0 • Dropout - first fully connected layer Wednesday, April 9, 14

  32. Normalization • ReLU - unbounded • Normalize features to [0, 1] based on holdout Wednesday, April 9, 14

  33. Verification Metrics • Unsupervised - dot product • χ 2 similarity • Siamese network Wednesday, April 9, 14

  34. Χ 2 Similarity • Χ 2 (f 1 ,f 2 ) = Σ i w i (f 1 [i] - f 2 [i]) 2 /(f 1 [i] + f 2 [i]) • weights learned via svm Wednesday, April 9, 14

  35. Siamese Network FC 4096-to-1 - Wednesday, April 9, 14

  36. Datasets • Social Face Classification (SFC) • Presumably Facebook photos • 4.4 mil faces; 4,030 people • No overlap with other datasets Wednesday, April 9, 14

  37. Datasets • Labeled Faces in the Wild (LFW) • 13,323 faces; 5,749 celebs • 6,000 pairs • Restricted protocol - same/not same labels at training • Unrestricted protocol - identities during training • Unsupervised - no training on LFW Wednesday, April 9, 14

  38. Datasets • YouTube Faces (YTF) • 3,425 videos of 1,595 subjects • Subset of celebs from LFW Wednesday, April 9, 14

  39. SFC Training Perf Reduce data by Reduce data by Remove layers omitting people omitting examples from network Wednesday, April 9, 14

  40. LFW Perf Wednesday, April 9, 14

  41. Runtime • 0.18 s - feature extraction (1 core; 2.2 GHz) • 0.05 s - alignment • 0.33 s - total Wednesday, April 9, 14

  42. Questions? Wednesday, April 9, 14

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