The goal of Computer Vision is to enable the computer or AI agent to ‘see’ and ‘understand’ the world like if not better than human beings. To that end, the computer needs to use sensors such as RGB or depth sensors to interact with the world. It mainly includes the understanding of the environment (scene understanding), humans, and further interaction with humans. Mapping to specific computer vision problems, this course will cover advanced topics in computer vision, such as 1) Scene Understanding, 2)Graphical Models, 3)3D visual perception , 4) Human Analysis and modeling. This course will review current research literature in the above fields and update students with state-of-the-art techniques. Students will work on group projects related to concrete research problems and present their research results in the form of seminars.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Describe and analyze the main research challenges in the field of computer vision.
- Summarize research literature and state-of-the-art techniques for solving the challenging research problems in those areas.
- Model and formulate problems, propose effective solutions to the problem and implement algorithms using suitable programming languages.
- Design network structure and loss functions in cases where problems need to be solved using deep learning techniques.
- Analyze the results and effectively evaluate the results on benchmark datasets.
Indicative Assessment
- Literature Reading Reports (50) [LO null]
- Research/Design Project (30) [LO null]
- Seminar Presentation (20) [LO null]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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Workload
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
not required
Preliminary Reading
The reading list includes the most recent following conference proceedings for computer vision and machine learning, which are
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
International Conference on Computer Vision (ICCV)
European Conference on Computer Vision (ECCV)
Advances in Neural Information Processing Systems (NIPS)
International Conference on Learning Representations (ICLR)
Assumed Knowledge
Students are expected to have experience in undergraduate-level mathematics and programming skills. Basic knowledge of computer vision and/or machine learning, for example acquired through the courses Computer Vision and/or Statistical Machine Learning is also required.
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4320 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 9516 | 27 Jul 2020 | 03 Aug 2020 | 31 Aug 2020 | 30 Oct 2020 | In Person | N/A |