Images do not appear in isolation. For example, on the web images are typically surrounded by informative text in the form of tags (e.g., on Flickr), captions (short summaries conveying something about the picture), and blogs/news articles, etc. In robotics, language is the most convenient way to teach an autonomous agent novel concepts or to communicate the mistakes it is making. For example, when providing a novel task to a robot, such as "pass me the stapler", we could provide additional information, e.g., "it is next to the beer bottle on the table". This information could be used to greatly simplify the parsing task. Conversely, it also crucial that the agent communicates its understanding of the scene to the human, e.g., "I can't, I am watching tv on a sofa, next to the wine bottle."

This class is a graduate seminar course in computer vision. The class will focus on the topic of visual recognition by exploiting textual information. We will discuss various problems and applications in this domain, and survey the current papers on the topic of images/videos and text. The goal of the class will be to understand the cross-domain approaches, to analyze their strengths and weaknesses, as well as to identify interesting new directions for future research.

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

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When emailing us, please put CSC2539 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.

Please sign up here in the beginning of class.

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  • William Tunstall-Pedoe, British entrepreneur:    Lecture on Question-Answering
  • Frank Rudzicz, Scientist at Toronto Rehabilitation Institute, Assistant Professor at University of Toronto:    Lecture on Natural Language Processing
  • Jamie Kiros, PhD student, University of Toronto:    Lecture on Fun with Vision and Language
  • Makarand Tapaswi, Postdoctoral fellow, University of Toronto:    Lecture on Story Understanding
  • Kaustav Kundu, PhD student, University of Toronto:    Lecture on Datasets and metrics for image captioning

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%

Detailed Requirements   

Paper reviewing

Every week (except for the first two) we will read 2 to 3 papers. The success of the discussion in class will thus be due to how prepared the students come to class. Each student is expected to read all the papers that will be discussed and write two detailed reviews about the selected two papers. Depending on enrollment, each student will need to also present a paper in class. When you present, you do not need to hand in the review.

Deadline: The reviews will be due one day before the class.

Structure of the review
Short summary of the paper
Main contributions
Positive and negatives points
How strong is the evaluation?
Possible directions for future work


Depending on enrollment, each student will need to present a few papers in class. The presentation should be clear and practiced and the student should read the assigned paper and related work in enough detail to be able to lead a discussion and answer questions. Extra credit will be given to students who also prepare a simple experimental demo highlighting how the method works in practice.

A presentation should be roughly 20 minutes long (please time it beforehand so that you do not go overtime). Typically this is about 15 to 20 slides. You are allowed to take some material from presentations on the web as long as you cite the source fairly. In the presentation, also provide the citation to the paper you present and to any other related work you reference.

Deadline: The presentation should be handed in one day before the class (or before if you want feedback).

Structure of presentation:
High-level overview with contributions
Main motivation
Clear statement of the problem
Overview of the technical approach
Strengths/weaknesses of the approach
Overview of the experimental evaluation
Strengths/weaknesses of evaluation
Discussion: future direction, links to other work


Each student will need to write a short project proposal in the beginning of the class (in January). The projects will be research oriented. In the middle of semester course you will need to hand in a progress report. One week prior to the end of the class the final project report will need to be handed in and presented in the last lecture of the class (April). This will be a short, roughly 15-20 min, presentation.

The students can work on projects individually or in pairs. The project can be an interesting topic that the student comes up with himself/herself or with the help of the instructor. The grade will depend on the ideas, how well you present them in the report, how well you position your work in the related literature, how thorough are your experiments and how thoughtful are your conclusions.

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We will first survey a few current methods on visual object recognition and scene understanding, as well as basic Natural Language Processing. The main focus of the course will be on vision and how to exploit natural language to learn visual concepts, improve visual parsing, do retrieval, as well as lingual description generation.

Tentative Syllabus   

Image/Video understanding
object recognition
image labeling
scene understanding
Natural Language Processing
parsing, part-of-speech tagging
coreference resolution
Vision and Language
learning visual models from text
visual-text alignment
zero-shot recognition
image generation (from text)
visual explanations
visual word-sense disambiguation
close Syllabus

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Jan 11Intro  Jamie Kiros
(invited lecture)
Jan 18Basics and Popular Topics in NLP Frank Rudzicz
(invited lecture)
Readings on Images/Videos and Text
Jan 25Image Captioning: Datasets and metricsKaustav Kunduslides
Image CaptioningUnifying Visual-Semantic Embeddings with Multimodal Neural Language Models [PDF]
R. Kiros, R. Salakhutdinov, R. S. Zemel
David Madrasslides
Feb 1Image CaptioningShow, Attend and Tell: Neural Image Caption Generation with Visual Attention [PDF]
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
Katherine Geslides
Paragraph GenerationA Hierarchical Approach for Generating Descriptive Image Paragraphs [PDF]
Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei
Tianyang Liuslides
Feb 8Dialog SystemsVisual Dialog [PDF]
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, Jose M. F. Moura, Devi Parikh, Dhruv Batra
Sayyed Nezhadislides
Domain adaptationLearning Aligned Cross-Modal Representations from Weakly Aligned Data [PDF]
L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, A. Torralba
Lluis Castrejon
Feb 14Question-AnsweringQuestion-Answering in IndustryWilliam Tunstall-Pedoe
(invited talk)
Feb 15Question-AnsweringAsk Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources [PDF]
Qi Wu, Peng Wang, Chunhua Shen, Anthony Dick, Anton van den Hengel
Paul Vicolslides
RetrievalOrder-Embeddings of Images and Language [PDF]
Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
Aryan Arbabislides
Mar 1Understanding DiagramsA Diagram Is Worth A Dozen Images [PDF]
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi
Ramin Zaviehgardslides
0-shot LearningPredicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions [PDF]
Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
Fartash Faghrislides

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Tutorials, related courses:



Online demos:

Main conferences:

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