Spring 2019 CS543/ECE549 Projects
Proposals due March 11, progress reports due April 15, final reports due May 6
C. Van Loo, Architecture, 1752
The goal of your project is to try something new and, perhaps, to contribute something to the
field of computer vision. Projects should be done in groups of two to four people. Individual projects require the instructor's approval. Feel free to use the "Search for Teammates" topic on Piazza
to form teams. There is also a "project" tag for discussing project ideas. And of course, you're encouraged to discuss possible topics with us during office hours.
Here are some ideas for getting started on projects:
For inspiration, you may want to take a look at the list of topics from the last several years of this class.
- Select a paper from the computer vision literature, implement and test the approach described in that paper.
- Take some publicly available code, apply it to an interesting dataset and explore various extensions and modifications. You may also want to compare two or more systems. Merely running existing code on the data provided by the authors is not sufficient.
- Design and implement a solution to a problem that interests you. It is fine to pick a problem related to your RA or dissertation research, but be sure to formulate a clear short-term goal and desired outcome for the class project. Ideally, you should use the class project as an opportunity to try new techniques that you might not consider or have a chance to use otherwise.
- Find a competition or dataset on Kaggle.
Proposal: due Monday, March 11, 11:59:59PM
Your proposal should consist of two to three pages and address each of the following:
- List of group members.
- Project description and goals: Describe the problem you plan to solve and give a basic outline of what you propose to implement. You can change later as you go along, but try to think this through as much as possible in advance. Identify the desired final outcome and pose maximum and minimum goals.
- Member roles: Indicate which project component each group member will be responsible for and how the group will interact.
Resources: Specify what data you plan to use -- include examples of images and any required annotations. What is your implementation platform? Do you plan to use any outside code and how do you plan to build on it? Be specific and give pointers to all relevant resources.
Reservations: Try to anticipate which part of the implementation or testing may prove the most difficult. Possible stumbling blocks shouldn't necessarily prevent you from attempting a more ambitious project, but you should make sure that you have a realistic "minimum" goal that can be accomplished.
Relationship to your background: Describe how your proposed project relates to your background or level of knowledge. Which techniques, software packages, etc., are you already familiar with, and which ones will be new to you? If the project is related to your dissertation or RA work, describe the relationship and the amount of overlap. Grading of your project will be relative to your background. That is, we will take into account not only the absolute outcomes of your project, but how much new material you have had to master and how successfully you have mastered it.
The proposal in PDF format should be uploaded on Compass by one designated group member (but make sure that everybody's names are on the document).
The proposal is not graded separately, but late submission will incur a 10% per day penalty on the final project grade.
Progress report: due April 15, 11:59:59PM
The progress report should be two-four pages and should contain the following:
- An updated statement of the project definition and goals. If there are any changes, discuss them specifically, together with the reasons for these changes.
- Current member roles and collaboration strategy. Who is responsible for what? How are the code and data shared/maintained? How does the group interact -- e.g., are there regularly scheduled meetings?
- Proposed approach in the form of a detailed outline, pseudocode, or prose description. Be specific about how you plan to implement each step with references, pointers to external code, etc. One or more references are required at this stage.
- Data: specific description, including number of images, type of images and annotations, URL (if applicable), etc. Examples of your actual data are required.
- Initial results: brief description of which steps (if any) from #3 above you have already implemented. As a minimum, you should have collected your data (or have the collection process well underway) and compiled and tested any external code.
- Current reservations and questions (if any).
The progress report should be uploaded on Compass by one designated group member (but make sure that everybody's names are on the document). The report will not receive a separate grade, but its content and quality will contribute holistically to the overall project grade at the end of the semester. Late submission will incur a 10% per day penalty on the final project grade.
Final report: due May 6, 11:59:59PM
The final report should be submitted in PDF format by one designated group member on Compass.
It should be (the equivalent of) at least six pages (single-spaced, 10 point font) and mimic
the style of a research paper. Here is an outline to follow for the report:
In addition to the report, please submit a zip file of your source code. We are looking for only the most important code that you wrote yourself for the project, not any external libraries. We do not plan to run the code, but want to see it to be able to verify that you actually did the work you claim to have done.
- Introduction: Define and motivate the problem, discuss background material or related work, and
briefly summarize your approach.
- Details of the approach: Include any formulas, pseudocode, diagrams -- anything
that is necessary to clearly explain your system and what you have done. If possible, illustrate
the intermediate stages of your approach with results images.
- Results: Clearly describe your experimental protocols. If you are using training and
test data, report the numbers of training and test images. Be sure to include example output figures.
Quantitative evaluation is always a big plus (if applicable). If you are working with
videos, put example output on YouTube or some other external repository and include links in your
- Discussion and conclusions: Summarize the main insights drawn from your analysis and
experiments. You can get a good project grade with mostly negative results, as long as you show evidence of extensive
exploration, thoughtfully analyze the causes of your negative results, and discuss potential
- Statement of individual contribution: Required if there is more than one group member.
- References: including URLs for any external code or data used.
Grades will be based on the quality of the project (originality, thoroughness, extent of analysis,
etc.) and the clarity of the written report and presentation. Ideally, you will try
something new or apply ideas from class to your domain or research. More will be expected of
larger groups. You can still get a good grade if your ideas do not work out, as long as your presentation and
report show evidence of extensive analysis and exploration, and provides thoughtful explanations
of the observed outcomes.