Video Analysis with Convolutional Neural Networks (Master Computer Vision Barcelona 2017)

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Module 4 - Lecture 6

Video Analysis with CNNs31 January 2017

Xavier Giró-i-Nieto

[http://pagines.uab.cat/mcv/]

Acknowledgments

Víctor Campos Alberto Montes

Linked slides

Outline

1. Recognition2. Optical Flow3. Object Tracking4. Audio and Video5. Generative models

Recognition

Demo: Clarifai

MIT Technology Review : “A start-up’s Neural Network Can Understand Video” (3/2/2015)5

Figure: Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014, June). Large-scale video classification with convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on (pp. 1725-1732). IEEE.

Recognition

Figure: Tran, Du, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. "Learning spatiotemporal features with 3D convolutional networks." In Proceedings of the IEEE International Conference on Computer Vision, pp. 4489-4497. 2015

Recognition

Previous lectures

Recognition

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014, June). Large-scale video classification with convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on (pp. 1725-1732). IEEE.

Slides extracted from ReadCV seminar by Victor Campos 10

Recognition: DeepVideo

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014, June). Large-scale video classification with convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on (pp. 1725-1732). IEEE. 11

Recognition: DeepVideo: Demo

Recognition: DeepVideo: Architectures

Unsupervised learning [Le at al’11] Supervised learning [Karpathy et al’14]

Recognition: DeepVideo: Features

Recognition: DeepVideo: Multiscale

Recognition: DeepVideo: Results

Recognition

Recognition: C3D

18Tran, Du, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. "Learning spatiotemporal features with 3D convolutional networks." In Proceedings of the IEEE International Conference on Computer Vision, pp. 4489-4497. 2015

Recognition: C3D: Demo

19K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition ICLR 2015.

Recognition: C3D: Spatial dimensionSpatial dimensions (XY) of the used kernels are fixed to 3x3, following Symonian & Zisserman (ICLR 2015).

Recognition: C3D: Temporal dimension3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets

Temporal depth

2D ConvNets

A homogeneous architecture with small 3 × 3 × 3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets

Recognition: C3D: Temporal dimension

No gain when varying the temporal depth across layers.

Recognition: C3D: Temporal dimension

Recognition: C3D: Architecture

Featurevector

Recognition: C3D: Feature vector

Video sequence

16 frames-long clips

8 frames-long overlap

Recognition: C3D: Feature vector

16-frame clip

Average

L2 norm

Recognition: C3D: VisualizationBased on Deconvnets by Zeiler and Fergus [ECCV 2014] - See [ReadCV Slides] for more details.

Recognition: C3D: Compactness

Convolutional 3D(C3D) combined with a simple linear classifier outperforms state-of-the-art methods on 4 different benchmarks and are comparable with state of the art methods on other 2 benchmarks

Recognition: C3D: Performance

Recognition: C3D: SoftwareImplementation by Michael Gygli (GitHub)

30Simonyan, Karen, and Andrew Zisserman. "Two-stream convolutional networks for action recognition in videos." 2014.

Recognition: Two stream Two CNNs in paralel:

● One for RGB images● One for Optical flow (hand-crafted features)

Fusion after the softmax layer

31Feichtenhofer, Christoph, Axel Pinz, and Andrew Zisserman. "Convolutional two-stream network fusion for video action recognition." CVPR 2016. [code]

Recognition: Two stream Two CNNs in paralel:

● One for RGB images● One for Optical flow (hand-crafted features)

Fusion at a convolutional layer

32Shou, Zheng, Dongang Wang, and Shih-Fu Chang. "Temporal action localization in untrimmed videos via multi-stage cnns." CVPR 2016.(Slidecast and Slides by Alberto Montes)

Recognition: Localization

Shou, Zheng, Dongang Wang, and Shih-Fu Chang. "Temporal action localization in untrimmed videos via multi-stage cnns." CVPR 2016.(Slidecast and Slides by Alberto Montes)

Recognition: Localization

Shou, Zheng, Dongang Wang, and Shih-Fu Chang. "Temporal action localization in untrimmed videos via multi-stage cnns." CVPR 2016.(Slidecast and Slides by Alberto Montes)

Outline

Optical Flow

Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013, December). DeepFlow: Large displacement optical flow with deep matching. In Computer Vision (ICCV), 2013 IEEE International Conference on (pp. 1385-1392). IEEE 36

Optical Flow: DeepFlow

Andrei BursucPostoc INRIA@abursuc

Optical Flow: DeepFlow

● Deep (hierarchy) ✔

● Convolution ✔

● Learning ❌

Optical Flow: Small vs Large

Optical FlowClassic approach:Rigid matching of HoG or SIFT descriptors

Deep Matching:Allow each subpatch to move:

● independently● in a limited range

depending on its size

Optical Flow: Deep Matching

Source: Matlab R2015b documentation for normxcorr2 by Mathworks42

Optical Flow: 2D correlation

Sub-Image

Offset of the sub-image with respect to the image [0,0].

Instead of pre-trained filters, a convolution is defined between each:

● patch of the reference image● target image

...as a results, a correlation map is generated for each reference patch.

The most discriminative response map

The less discriminative

response map

Key idea: Build (bottom-up) a pyramid of correlation maps to run an efficient (top-down) search.

4x4 patches

8x8 patches

16x16 patches

32x32 patches

Top-down matching

(TD)Bottom-upextraction

4x4 patches

8x8 patches

16x16 patches

32x32 patches

Bottom-upextraction

Optical Flow: Deep Matching (BU)

Optical Flow: Deep Matching (TD)

4x4 patches

8x8 patches

16x16 patches

32x32 patches

Top-down matching

Optical Flow: Deep Matching (TD)Each local maxima in the top layer corresponds to a shift of one of the biggest (32x32) patches.If we focus on local maximum, we can retrieve the corresponding responses one scale below and focus on shift of the sub-patches that generated it

Optical Flow: Deep Matching (TD)

Ground truth

Dense HOG[Brox & Malik 2011]

Deep Matching

Optical Flow

Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D. and Brox, T., 2015. FlowNet: Learning Optical Flow With Convolutional Networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2758-2766). 54

Optical Flow: FlowNet

End to end supervised learning of optical flow.

Optical Flow: FlowNet (contracting)

Option A: Stack both input images together and feed them through a generic network.

Option B: Create two separate, yet identical processing streams for the two images and combine them at a later stage.

Correlation layer: Convolution of data patches from the layers to combine.

Optical Flow: FlowNet (expanding)

Upconvolutional layers: Unpooling features maps + convolution.Upconvolutioned feature maps are concatenated with the corresponding map from the contractive part.

Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D. and Brox, T., 2015. FlowNet: Learning Optical Flow With Convolutional Networks. ICCV 2015 61

Since existing ground truth datasets are not sufficiently large to train a Convnet, a synthetic Flying Dataset is generated… and augmented (translation, rotation, scaling transformations; additive Gaussian noise; changes in brightness, contrast, gamma and color).

Convnets trained on these unrealistic data generalize well to existing datasets such as Sintel and KITTI.

Data augmentation

Outline

Object tracking: MDNet

64Nam, Hyeonseob, and Bohyung Han. "Learning multi-domain convolutional neural networks for visual tracking." ICCV VOT Workshop (2015)

Object tracking: MDNet

Object tracking: MDNet: Architecture

Domain-specific layers are used during training for each sequence, but are replaced by a single one at test time.

Object tracking: MDNet: Online update

MDNet is updated online at test time with hard negative mining, that is, selecting negative samples with the highest positive score.

Object tracking: FCNT

68Wang, Lijun, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. "Visual Tracking with Fully Convolutional Networks." ICCV 2015 [code]

Object tracking: FCNT

69Wang, Lijun, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. "Visual Tracking with Fully Convolutional Networks." In Proceedings of the IEEE International Conference on Computer Vision, pp. 3119-3127. 2015 [code]

Focus on conv4-3 and conv5-3 of VGG-16 network pre-trained for ImageNet image classification.

conv4-3 conv5-3

Object tracking: FCNT: Specialization

Most feature maps in VGG-16 conv4-3 and conv5-3 are not related to the foreground regions in a tracking sequence.

Object tracking: FCNT: Localization

Although trained for image classification, feature maps in conv5-3 enable object localization…...but is not discriminative enough to different objects of the same category.

Object tracking: Localization

72Zhou, Bolei, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Object detectors emerge in deep scene cnns." ICLR 2015.

Other works have shown how features maps in convolutional layers allow object localization.

Object tracking: FCNT: Localization

On the other hand, feature maps from conv4-3 are more sensitive to intra-class appearance variation…

conv4-3 conv5-3

Object tracking: FCNT: Architecture

SNet=Specific Network (online update)

GNet=General Network (fixed)

Object tracking: FCNT: Results

Outline

Audio and Video

Audio Vision

Audio and Video: Soundnet

Aytar, Yusuf, Carl Vondrick, and Antonio Torralba. "Soundnet: Learning sound representations from unlabeled video." In Advances in Neural Information Processing Systems, pp. 892-900. 2016.

Object & Scenes recognition in videos by analysing the audio track (only).

79Aytar, Yusuf, Carl Vondrick, and Antonio Torralba. "Soundnet: Learning sound representations from unlabeled video." NIPS 2016.

Videos for training are unlabeled. Relies on CNNs trained on labeled images.

81Aytar, Yusuf, Carl Vondrick, and Antonio Torralba. "Soundnet: Learning sound representations from unlabeled video." In Advances in Neural Information Processing Systems, pp. 892-900. 2016.

Hidden layers of Soundnet are used to train a standard SVM classifier that outperforms state of the art.

Visualization of the 1D filters over raw audio in conv1.

86Aytar, Yusuf, Carl Vondrick, and Antonio Torralba. "Soundnet: Learning sound representations from unlabeled video." In Advances in Neural Information Processing Systems, pp. 892-900. 2016.

Visualization of the video frames associated to the sounds that activate some of the last hidden units (conv7):

Audio and Video: Sonorizaton

Owens, Andrew, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H. Adelson, and William T. Freeman. "Visually indicated sounds." CVPR 2016.

Learn synthesized sounds from videos of people hitting objects with a drumstick.

Audio and Video: Visual Sounds

No end-to-end

Audio and Video: Visual Sounds

Learn moreRuus Salakhutdinov, “Multimodal Machine Learning” (NIPS 2015 Workshop)

Generative models for Video

SlidesD2L5 by Santi Pascual.

What are Generative Models?

We want our model with parameters θ = {weights, biases} and outputs

distributed like Pmodel to estimate the distribution of our training data Pdata.

Example) y = f(x), where y is scalar, make Pmodel similar to Pdata by training

the parameters θ to maximize their similarity.

Key Idea: our model cares about what distribution generated the input data

points, and we want to mimic it with our probabilistic model. Our learned

model should be able to make up new samples from the distribution, not

just copy and paste existing samples!

What are Generative Models?

Figure from NIPS 2016 Tutorial: Generative Adversarial Networks (I. Goodfellow)

Video Frame Prediction

Mathieu, Michael, Camille Couprie, and Yann LeCun. "Deep multi-scale video prediction beyond mean square error." ICLR 2016

Adversarial Training analogy

Imagine we have a counterfeiter (G) trying to make fake money, and the police (D) has to detect whether money is real or fake.

It’s not even green

There is no watermark

Watermark should be rounded

After enough iterations, and if the counterfeiter is good enough (in terms of G network it means “has enough parameters”), the police should be confused.

Adversarial Training (batch update)

● Pick a sample x from training set● Show x to D and update weights to

output 1 (real)

● G maps sample z to ẍ ● show ẍ and update weights to output 0 (fake)

● Freeze D weights● Update G weights to make D output 1 (just G weights!)● Unfreeze D Weights and repeat

Generative Adversarial Networks (GANs)

Slide credit: Víctor Garcia

DiscriminatorD(·)

GeneratorG(·)

Real World

Randomseed (z)

Real/Synthetic

105Slide credit: Víctor Garcia

Conditional Adversarial Networks

Real World

Real/Synthetic

Condition

DiscriminatorD(·)

GeneratorG(·)

Generating images/frames

(Radford et al. 2015)

Deep Conv. GAN (DCGAN) effectively generated 64x64 RGB images in a single shot. For example bedrooms from LSUN dataset.

Generating images/frames conditioned on captions

(Reed et al. 2016b) (Zhang et al. 2016)

Unsupervised feature extraction/learning representations

Similarly to word2vec, GANs learn a distributed representation that disentangles concepts such that we can perform operations on the data manifold:

v(Man with glasses) - v(man) + v(woman) = v(woman with glasses)

(Radford et al. 2015)

Image super-resolution

Bicubic: not using data statistics. SRResNet: trained with MSE. SRGAN is able to understand that there are multiple correct answers, rather than averaging.

(Ledig et al. 2016)

Image super-resolution

Averaging is a serious problem we face when dealing with complex distributions.

(Ledig et al. 2016)

Manipulating images and assisted content creation

https://youtu.be/9c4z6YsBGQ0?t=126 https://youtu.be/9c4z6YsBGQ0?t=161

(Zhu et al. 2016)

Adversarial Networks

Slide credit: Víctor Garcia

Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. "Image-to-image translation with conditional adversarial networks." arXiv preprint arXiv:1611.07004 (2016).

Generator

Discriminator

Generated Pairs

Real World

Ground Truth Pairs

Loss → BCE

113Víctor Garcia and Xavier Giró-i-Nieto (work under progress)

Generator

Discriminator Loss2 GAN {Binary Crossentropy}

Generative models for video

114Vondrick, Carl, Hamed Pirsiavash, and Antonio Torralba. "Generating videos with scene dynamics." 2016.

Generative models for video

115Vondrick, Carl, Hamed Pirsiavash, and Antonio Torralba. "Generating videos with scene dynamics." NIPS 2016.

Adversarial Networks

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial nets." NIPS 2014Goodfellow, Ian. "NIPS 2016 Tutorial: Generative Adversarial Networks." arXiv preprint arXiv:1701.00160 (2016).

F. Van Veen, “The Neural Network Zoo” (2016)

Outline

Thank you !

https://imatge.upc.edu/web/people/xavier-giro

https://twitter.com/DocXavi

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xavier.giro@upc.edu

Xavier Giró-i-Nieto

[Part B: Video and audio]

Video Analysis with Convolutional Neural Networks (Master Computer Vision Barcelona 2017)

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