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Lecture 08 larry zitnick - undestanding and describing scenes
This document summarizes the history of computer vision and object recognition from the 1970s to the present. It discusses early works on edge detection and perceptual organization. It then covers major milestones like handwritten digit recognition using backpropagation, face detection, the SIFT feature descriptor, and object detection using HOG features and deformable part models. It notes the improvements brought by large datasets like ImageNet and algorithms like deep convolutional neural networks, which achieved breakthrough performance on ImageNet classification in 2012. It concludes by discussing challenges going forward like detection in context and generating rich image descriptions.
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Lecture 08 larry zitnick - undestanding and describing scenes
- 1. 7/4/2014 1 Understanding and Describing Scenes LarryZitnick Microsoft Research Motivation How did we get here? Where are we going? Goals Will deep neural networks solve it all? 30 min: Current status After we recognize everything, what next? 40 min: Beyond recognition Why it works, why it fails! 45 min: History object recognition 1973 The representation and matching of pictorial structures, Fischler and Elschlager, 1973 a.k.a. constellation model
- 2. 7/4/2014 2 1973 1973 1980’s AI winter……back to basics A Computational Approach to Edge Detection, Canny 1986 Perceptual Organization and Visual Recognition, David Lowe, 1984 1984
- 3. 7/4/2014 3 Perceptual organization andthe representation of natural form, Alex Pentland, 1986 1986 1989 Zip codes MNIST Backpropagation applied to handwritten zip code recognition, Lecun et al., 1989 Image Pooling Convolution 1989 1989 Backpropagation applied to handwritten zip code recognition, Lecun et al., 1989
- 4. 7/4/2014 4 1998 Neural Network-Based FaceDetection, Rowley at al., PAMI 1998 Faces Limits Numbers worked great, and so did faces… Why did other categories fail? 2 main reasons… (2012) 2001 Rapid Object Detection using a Boosted Cascade of Simple Features, Viola and Jones, CVPR 2001 Boosting + Cascade = Speed Why did it work? • Simple features (Haar wavelets) Integral images + Haar wavelets = fast - = h
- 5. 7/4/2014 5 Why did itwork? Why did it fail?
- 6. 7/4/2014 6 1999* Object Recognition fromLocal Scale-Invariant Features, Lowe, ICCV 1999. SIFT (scale invariant feature transform) No more sliding windows (interest points) Better features (use more computation) Difference of Gaussians (DoG): Difference of Gaussians (DoG): Distinctive image features from scale-invariant keypoints, Lowe, IJCV 2004 Better descriptor:
- 7. 7/4/2014 7 Truncated normalization 0.2 Distinctive imagefeatures from scale-invariant keypoints, Lowe, IJCV 2004 “Object instance” recognition What worked • Flat planer objects with lots of texture. • Panorama stitching Recognizing panoramas, Brown and Lowe, ICCV 2003 What failed…
- 8. 7/4/2014 8 2003 Constellation model(redux) Object Class Recognition by Unsupervised Scale-Invariant Learning, Fergus et al., CVPR 2003. 2003 Constellation model (redux) Object Class Recognition by Unsupervised Scale-Invariant Learning, Fergus et al., CVPR 2003. The representationand matching of pictorial structures, Fischler and Elschlager, 1973 Interest points used to find parts: Smaller number of candidate parts allows for more complex spatial models. Why does it fail?
- 9. 7/4/2014 9 Why it fails •Interest points don’t work for category recognition Too many springs… Why it worked 2005 HOG (histograms of oriented gradients) Histograms of oriented gradients for human detection, Dalal and Triggs, CVPR 2005.
- 10. 7/4/2014 10 Pedestrians • Defined bytheir contours • Cluttered backgrounds • Significant variance in texture Interest points won’t work… …back to sliding window. 2005 HOG (histograms of oriented gradients) 2005 HOG (histograms of oriented gradients) 2005 HOG (histograms of oriented gradients) Histograms of oriented gradients for human detection, Dalal and Triggs, CVPR 2005.
- 11. 7/4/2014 11 2005 HOG (histogramsof oriented gradients) Normalize locally not globally 2005 HOG (histograms of oriented gradients) Presence > Magnitude 2005 HOG (histograms of oriented gradients) NMS Linear SVM classifier For every candidate bounding box Compute HOG features Non-maximal suppression Why it worked We can finally detect object boundaries in a reliable manner! Computers are fast enough. Hard negative mining
- 12. 7/4/2014 12 Why it failed2006 Context People don’t walk in the sky… Putting Objects in Perspective, Hoiem, Efros, and Hebert, CVPR 2006. Why it worked • Our world is not random • Some contextual cues may be reliably found Why it failed • Many contextual cues are hard to detect • We need more data • Doesn’t help with false negatives
- 13. 7/4/2014 13 2004-2006 Caltech 101and 256 2006 15-30 training images, up to ~70% accuracy. Antonio Torralba 2007 PASCAL VOC The PASCAL Visual Object Classes (VOC) Challenge, Everingham, Van Gool, Williams, Winn and Zisserman, IJCV, 2010 20 classes 2009 Caltech Pedestrian Pedestrian Detection: An Evaluation of the State of the Art, Dollár, Wojek, Schiele and Perona, PAMI, 2012 1 class, lots of instances.
- 14. 7/4/2014 14 2009 ImageNet ImageNet: ALarge-Scale Hierarchical Image Database, Deng, Dong, Socher, Li, Li and Fei-Fei, CVPR, 2009 Corgi Orb weaving spider 22K categories, 14M images 2010 SUN SUN Database: Large-scale Scene Recognition from Abbey to Zoo Xiao, Hays, Ehinger, Oliva, and Torralba, CVPR, 2010. 908 scene categories Beer garden 2008 DPM (Deformable parts model) Object Detection with Discriminatively Trained Part Based Model, Felzenszwalb, Girshick, McAllester and Ramanan, PAMI, 2010
- 15. 7/4/2014 15 2008 DPM (Deformableparts model) Object Detection with Discriminatively Trained Part Based Model, Felzenszwalb, Girshick, McAllester and Ramanan, PAMI, 2010 Star-structure • Computationally efficient (distance transform) Distance transforms of sampled functions, Felzenszwalb and Huttenlocher, Cornell University CIS, Tech. Rep. 2004. Multiple components Why it worked • Multiple components • Deformable parts? • Hard negative mining • Good balance "How important are 'Deformable Parts' in the Deformable Parts Model?“, Divvala, Efros, and Hebert, Parts and Attributes Workshop, ECCV, 2012
- 16. 7/4/2014 16 Why it failed… Humandebugging Low resolution 20x20 pixels Head Leg Feet Head ? Is it a head, torso, arm, leg, foot, hand, or nothing? Nothing Finding the Weakest Link in Person Detectors, Parikh and Zitnick, CVPR 2011. Part detections Humans Machine Head Torso Arm Hand Leg Foot Person Part detections Humans Machine Head Torso Arm Hand Leg Foot Person
- 17. 7/4/2014 17 Part detections Humans Machine Head TorsoArm Hand Leg Foot Person AP results Finding the Weakest Link in Person Detectors, Parikh and Zitnick, CVPR 2011. Do We Need More Training Data or Better Models for Object Detection? Zhu, Vondrick, Ramanan, Fowlkes, BMVC 2012. HOG SVM Pooling Image DPM Limited capacity classifier Low-level features Something new?
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- 19. 7/4/2014 19 2012 ImageNet 1K (Fall2012) 0 5 10 15 20 25 30 35 40 Error 0 5 10 15 20 25 30 35 40 Error 2012 ImageNet 1K (Fall 2012) 2012 DNNs (deep neural networks) Image Output 90% parameters Imagenet Classification with Deep Convolutional Neural Networks, Krizhevsky, Sutskever, and Hinton, NIPS 2012 Imagenet Classification with Deep Convolutional Neural Networks, Krizhevsky, Sutskever, and Hinton, NIPS 2012 Gradient-Based Learning Applied to Document Recognition, LeCun, Bottou, Bengio and Haffner, Proc. of the IEEE, 1998 * Rectified activations and dropout
- 20. 7/4/2014 20 2014 DNN vs.Detection Aspect ratios? DNNs are slow… (relatively) 224 x 224 Finding object candidates Use low-level cues… Segmentation As Selective Search for Object Recognition, van de Sande, Uijlings, Gevers, Smeulders, ICCV 2011 Ross Girshick et al., Berkeley http://arxiv.org/pdf/1311.2524v1.pdf 1. Input image 2. Extract region proposals (~2k) 3. Compute CNN features 4. Classify regions Online classification demo: http://decaf.berkeleyvision.org/ Object detection
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- 24. 7/4/2014 24 PASCAL VOC ImageNet (200categories) ImageNet (200 categories)
- 25. 7/4/2014 25 Antonio Torralba, MIT Whyit fails… Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks, Oquab, Bottou, Laptev, Sivic, CVPR 2014
- 26. 7/4/2014 26 Why it fails… Intriguingproperties of neural networks, Szegedy, Zaremba, Sutskever, Bruna, Erhan, Goodfellow, Fergus, ICLR 2014. Correctly classified Incorrectly classified Why it fails… PANDA: Pose Aligned Networks for Deep Attribute Modeling, Zhang, Paluri, Ranzato, Darrell, Bourdev, CVPR 2014. DeepFace: closing the gap to human-Level performance in face verification, Taigman, Yang, Ranzato, Wolf, CVPR 2014. Finding object candidates Use low-level cues… Segmentation As Selective Search for Object Recognition, van de Sande, Uijlings, Gevers, Smeulders, ICCV 2011 Looking forward…
- 27. 7/4/2014 27 Microsoft COCO: CommonObjects in Context, Lin, Maire, Belongie, Hays, Perona, Ramanan, Dollár, Zitnick, ECCV 2014. http://mscoco.org Why a new dataset? Continue our field’s momentum: • Detecting difficult instances (context) • Segmentation • Enabling new research directions Detection in Context Instance segmentation Non-iconic instances Iconic object images
- 28. 7/4/2014 28 Iconic scene imagesNon-iconic images
- 29. 7/4/2014 29 MS COCO • 70-100object categories (things not stuff) • 330,000 images (~150k first release) • 2 million instances (400k people) • Every instance is segmented • 7.7 instances per image (3.5 categories) • Key points • 5 sentences per image http://mscoco.org Over 55,000 worker hours (6+ years) Post-recognition era Two professors converse in front of a blackboard.
- 30. 7/4/2014 30 Older Distinguished Blackboard (in front) Professor + Twoprofessors converse in front of a blackboard. Two professors stand in front of a blackboard. Two professors converse in front of a blackboard.
- 31. 7/4/2014 31 A crazy zebraclimbing a giraffe to get a better view. Computer Vision Common Sense Computer Vision Representation Sentence Generation ? Two professors converse in front of a blackboard. Person Person Dining table Felzenszwalb, 2010 Face Face Cat
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- 33. 7/4/2014 33 Generating data “Jenny justthrew the beach ball angrily at Mike while the dog watches them both.” Mike fights off a bear by giving him a hotdog while jenny runs away. Jenny and Mike are both playing dangerously in the park. Understanding sentences (Zitnick, Parikh, and Vanderwende, ICCV 2013) Jenny is catching the ball. Mike is kicking the ball. The table is next to the tree. Which visual features are semantically meaningful? (Zitnick and Parikh, CVPR 13) Temporal prediction (Fouhey and Zitnick, CVPR 2014)
- 34. 7/4/2014 34 A car crashinginto a SUV. Least visually informative words (single scenes) using isn’t doing went give behind before during onto through how since why finally almost today home me something attention Zitnick and Parikh, CVPR 2013 Context
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- 36. 7/4/2014 36 What happens next?Global motion models A data-driven approach for event prediction, Yuen and Torralba, ECCV 2010. Affordances People Watching: Human Actions as a Cue for Single View Geometry, Fouhey, ECCV 2012. Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation, Koppula et al., ICML 2013. Hallucinated Humans as the Hidden Context for Labeling 3D Scenes, Jiang et al., CVPR 2013. Patch to the Future: Unsupervised Visual Prediction, Walker et al., CVPR 2014
- 37. 7/4/2014 37 What moves? Whatmoves? What moves? What attributes change?
- 38. 7/4/2014 38 What attributes change?Do these move together? Do these move together? How do we collect training data?
- 39. 7/4/2014 39 Apparent Behavior, Heiderand Simmel, 1944 Apparent Behavior, Heider and Simmel, 1944 Data Gathered 5,000 sequences using Amazon’s mechanical Turk.
- 40. 7/4/2014 40 Make no assumptions… ? Likelihoodofmoving Boy’shorizontal position What we learned… Less Likely More Likely Visualizing the Model Visualizing the Model Less Likely More Likely
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- 42. 7/4/2014 42 Xinlei Chen, CMU CVAI Semantics Questions: What is the primary reason behind the recent success of deep neural networks in object recognition? (a) New approaches to learning using unsupervised training data. (b) New unit activation functions using rectification and the “dropout” learning technique. (c) More supervised training data and faster computationusing GPUs. What is one of the primary limitations of the deformable parts model? (a) Only a small number of deformations may be modeled. (b) HOG features combined with linear SVM classifiers are limited in their ability to detect object parts. (c) The limited amount of training data available in the PASCAL VOC dataset. What is a current limitation of deep neural networks when applied to object detection? (a) Objects that are small and primarily recognized by contextualinformation are not found. (b) Deep neural networks are not efficient when scaling to large numbers of object categories. (c) They do not generalize well across datasets.