Deep Learning in Computer Vision

In recent years, Deep Learning has become a dominant Machine Learning tool for a wide variety of domains. One of its biggest successes has been in Computer Vision where the performance in problems such object and action recognition has been improved dramatically. In this course, we will be reading up on various Computer Vision problems, the state-of-the-art techniques involving different neural architectures and brainstorming about promising new directions.

Please sign up here in the beginning of class.

This class is a graduate seminar course in computer vision. The class will cover a diverse set of topics in Computer Vision and various Neural Network architectures. It will be an interactive course where we will discuss interesting topics on demand and latest research buzz. The goal of the class is to learn about different domains of vision, understand, identify and analyze the main challenges, what works and what doesn’t, as well as to identify interesting new directions for future research.

Prerequisites: Courses in computer vision and/or machine learning (e.g., CSC320, CSC420, CSC411) are highly recommended (otherwise you will need some additional reading), and basic programming skills are required for projects.

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  • Time and Location

    Winter 2016

    Day: Tuesday
    Time: 9am-11am
    Room: ES B149 (Earth Science Building at 5 Bancroft Avenue)


    Sanja Fidler

    Email: fidler@cs dot toronto dot edu
    Office hours: by appointment (send email)

When emailing me, please put CSC2523 in the subject line.


This class uses piazza. On this webpage, we will post announcements and assignments. The students will also be able to post questions and discussions in a forum style manner, either to their instructors or to their peers.

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We will have an invited speaker for this course:

  • Raquel Urtasun
    Assistant Professor, University of Toronto
    Talk title: Deep Structured Models

as well as several invited lectures / tutorials:

  • Yuri Burda, Postdoctoral Fellow, University of Toronto:    Lecture on Variational Autoencoders
  • Ryan Kiros, PhD student, University of Toronto:    Lecture on Recurrent Neural Networks and Neural Language Models
  • Jimmy Ba, PhD student, University of Toronto:    Lecture on Neural Programming
  • Yukun Zhu, Msc student, University of Toronto:    Lecture on Convolutional Neural Networks
  • Elman Mansimov, Research Assistant, University of Toronto:    Lecture on Image Generation with Neural Networks
  • Emilio Parisotto, Msc student, University of Toronto:    Lecture on Deep Reinforcement Learning
  • Renjie Liao, PhD student, University of Toronto:    Lecture on Highway and Residual Networks
  • Urban Jezernik, PhD student, University of Ljubljana:    Lecture on Music Generation

Each student will need to write two paper reviews each week, present once or twice in class (depending on enrollment), participate in class discussions, and complete a project (done individually or in pairs).


The final grade will consist of the following
Participation (attendance, participation in discussions, reviews) 15%
Presentation (presentation of papers in class) 25%
Project (proposal, final report) 60%

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The first class will present a short overview of neural network architectures, however, the details will be covered when reading on particular topics. Readings will touch on a diverse set of topics in Computer Vision. The course will be interactive — we will add interesting topics on demand and latest research buzz.


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Date Topic Reading / Material Speaker Slides
Jan 12 Admin & Introduction(s) Sanja Fidler admin
Convolutional Neural Networks
Jan 19 Convolutional Neural Nets(tutorial) Resources: Stanford’s cs231 class, VGG’s Practical CNN Tutorial
Code: CNN Tutorial for TensorFlowTutorial for caffe, CNN Tutorial for Theano
Yukun Zhu
Image Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs   [PDF] [code]
L-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L Yuille
Shenlong Wang [pdf]
Jan 26 Very Deep Networks Highway Networks  [PDF] [code]
Rupesh Kumar Srivastava, Klaus Greff, Jurgen Schmidhuber

Deep Residual Learning for Image Recognition  [PDF]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

Renjie Liao
Object Detection Rich feature hierarchies for accurate object detection and semantic segmentation   [PDF] [code]
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks   [PDF] [code (Matlab)] [code (Python)]
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Kaustav Kundu [pdf]
Feb 2 Stereo
Siamese Networks
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches  [PDF] [code]
Jure Žbontar, Yann LeCun

Learning to Compare Image Patches via Convolutional Neural Networks  [PDF] [code]
Sergey Zagoruyko, Nikos Komodakis

Wenjie Luo [pdf]
Depth from Single Image Designing Deep Networks for Surface Normal Estimation   [PDF]
Xiaolong Wang, David Fouhey, Abhinav Gupta
Mian Wei [pptx]  [pdf]
Feb 9 Image Generation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks   [PDF]
Alec Radford, Luke Metz, Soumith Chintala

Generating Images from Captions with Attention   [PDF]
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov

Elman Mansimov
Domain Adaptation, Zero-shot Learning Simultaneous Deep Transfer Across Domains and Tasks   [PDF]
Eric Tzeng, Judy Hoffman, Trevor Darrell

Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions   [PDF]
Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov

Lluis Castrejon [pdf]
Recurrent Neural Networks
Feb 23 RNNs and Neural Language Models Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models   [PDF] [code]
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel

Skip-Thought Vectors   [PDF] [code]
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler

Jamie Kiros
Mar 1 Modeling Words Efficient Estimation of Word Representations in Vector Space  [PDF] [code]
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
Eleni Triantafillou [pdf]
Describing Videos Sequence to Sequence -- Video to Text   [PDF]
Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko
Erin Grant [pdf]
Image-based QA Ask Your Neurons: A Neural-based Approach to Answering Questions about Images   [PDF]
Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
Yunpeng Li [pdf]
Mar 8 Variational Autoencoders Auto-Encoding Variational Bayes   [PDF]
Diederik P Kingma, Max Welling

Tutorial: Bayesian Reasoning and Deep Learning   [PDF]
Shakir Mohamed

Yura Burda
Text-based QA End-To-End Memory Networks   [PDF]
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus
Marina Samuel [pdf]
Neural Reasoning Recursive Neural Networks Can Learn Logical Semantics   [PDF]
Samuel R. Bowman, Christopher Potts, Christopher D. Manning
Rodrigo Toro Icarte [pdf]
Mar 15 Neural Programming Neural GPUs Learn Algorithms   [PDF]
Lukasz Kaiser, Ilya Sutskever

Neural Programmer-Interpreters   [PDF]
Scott Reed, Nando de Freitas

Neural Programmer: Inducing Latent Programs with Gradient Descent   [PDF]
Arvind Neelakantan, Quoc V. Le, Ilya Sutskever

Jimmy Ba
Conversation Models A Neural Conversational Model   [PDF]
Oriol Vinyals, Quoc Le
Caner Berkay Antmen [pdf]
Sentiment Analysis Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank   [PDF]
Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts
Zhicong Lu [pdf]
Mar 22 Video Representations Unsupervised Learning of Video Representations using LSTMs  [PDF]
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
Kamyar Ghasemipour [pdf]
CNN Visualization Explaining and Harnessing Adversarial Examples   [PDF]
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Neill Patterson [pdf]
Mar 29 Direction Following (Robotics) Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences   [PDF]
Hongyuan Mei, Mohit Bansal, Matthew R. Walter
Alan Yusheng Wu [pdf]
Visual Attention Recurrent Models of Visual Attention   [PDF]
Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
Matthew Shepherd [pdf]
Music A First Look at Music Composition using LSTM Recurrent Neural Networks   [PDF]
Douglas Eck, Jurgen Schmidhuber

Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network   [PDF]
Andrew J.R. Simpson, Gerard Roma, Mark D. Plumbley

Charu Jaiswal [pdf]
Music generation Overview of music generation Urban Jezernik
Pose and Attributes PANDA: Pose Aligned Networks for Deep Attribute Modeling  [PDF]
Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
Sidharth Sahdev [pptx]
Image Style A Neural Algorithm of Artistic Style   [PDF]  [code]
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Nancy Iskander [pdf]
Apr 5 Human gaze Where Are They Looking?   [PDF]
Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
Abraham Escalante [pdf]
Instance Segmentation Monocular Object Instance Segmentation and Depth Ordering with CNNs   [PDF]
Ziyu Zhang, Alex Schwing, Sanja Fidler, Raquel Urtasun

Instance-Level Segmentation with Deep Densely Connected MRFs  [PDF]
Ziyu Zhang, Sanja Fidler, Raquel Urtasun

Min Bai [pdf]
Scene Understanding Attend, Infer, Repeat: Fast Scene Understanding with Generative Models   [PDF]
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, Geoffrey E. Hinton
Namdar Homayounfar [pdf]
Reinforcement Learning Playing Atari with Deep Reinforcement Learning   [PDF]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
Jonathan Chung [pdf]
Medical Imaging Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning   [PDF]
Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey
Alex Lu [pptx]
Humor We Are Humor Beings: Understanding and Predicting Visual Humor   [PDF]
Arjun Chandrasekaran, Ashwin K Vijayakumar, Stanislaw Antol, Mohit Bansal, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh
Shuai Wang [pdf]

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Tutorials, related courses:

  •   Introduction to Neural Networks, CSC321 course at University of Toronto
  •   Course on Convolutional Neural Networks, CS231n course at Stanford University
  •   Course on Probabilistic Graphical Models, CSC412 course at University of Toronto, advanced machine learning course



  •   Caffe: Deep learning for image classification
  •   Tensorflow: Open Source Software Library for Machine Intelligence (good software for deep learning)
  •   Theano: Deep learning library
  •   mxnet: Deep Learning library
  •   Torch: Scientific computing framework with wide support for machine learning algorithms
  •   LIBSVM: A Library for Support Vector Machines (Matlab, Python)
  •   scikit: Machine learning in Python


Popular datasets:

  •   ImageNet: Large-scale object dataset
  •   Microsoft Coco: Large-scale image recognition, segmentation, and captioning dataset
  •   Mnist: handwritten digits
  •   PASCAL VOC: Object recognition dataset
  •   KITTI: Autonomous driving dataset
  •   NYUv2: Indoor RGB-D dataset
  •   LSUN: Large-scale Scene Understanding challenge
  •   VQA: Visual question answering dataset
  •   Madlibs: Visual Madlibs (question answering)
  •   Flickr30K: Image captioning dataset
  •   Flickr30K Entities: Flick30K with phrase-to-region correspondences
  •   MovieDescription: a dataset for automatic description of movie clips
  •   Action datasets: a list of action recognition datasets
  •   MPI Sintel Dataset: optical flow dataset
  •   BookCorpus: a corpus of 11,000 books


Online demos:


Main conferences:

  •   NIPS (Neural Information Processing Systems)
  •   ICML (International Conference on Machine Learning)
  •   ICLR (International Conference on Learning Representations)
  •   AISTATS (International Conference on Artificial Intelligence and Statistics)
  •   CVPR (IEEE Conference on Computer Vision and Pattern Recognition)
  •   ICCV (International Conference on Computer Vision)
  •   ECCV (European Conference on Computer Vision)
  •   ACL (Association for Computational Linguistics)
  •   EMNLP (Conference on Empirical Methods in Natural Language Processing)


About Nguyễn Viết Hiền

Passionate, Loyal
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