Your class project is an opportunity for you to explore an interesting problem in the context of a real-world data sets in area of computer vision defined broadly. This means that topics such as machine learning over visual data, ways to interact with visual data, computational photography, computer graphics, language-vision problems, computer vision applied to domains such as medical images, and so on, are all acceptable.

Each project will be assigned a TA as a project mentor; instructors and TAs will consult with you on your ideas, but of course the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 20% of your final class grade, and will have three deliverables:

1. Proposal Abstract : 2 pages excluding references (2%)
2. Presentation : Poster presentation (3%)
3. Final Report : 8 pages excluding references (15%)

All write-ups should use the CVPR style.

Team Formation

You are responsible for forming project teams of two or three people. In some cases, we will also accept smaller or larger teams, but a 2-3 person group is preferred. If you have trouble forming a group, please send us an email and we will help you find project partners.

Scope

As a broad target, the final project should involve approximately as much work as a homework assignment for each student in the group. Thus the total work should scale roughly linearly with the group size, and be distributed roughly equally. An ambitious, well-done project from a group of two should be on the order of a conference paper in depth of experimentation.

Project Proposal

You must turn in a brief project proposal that provides an overview of your idea and also contains a brief survey of related work on the topic. We will provide a list of suggested project ideas for you to choose from, though you may discuss other project ideas with us, whether applied or theoretical. Note that even though you can use datasets you have used before, you cannot use work that you started prior to this class as your project.

Proposals should be approximately two pages long, and should include the following information:

• Project title and list of group members.
• Overview of project idea. This should be approximately half a page long.
• A short literature survey of 4 or more relevant papers. The literature review should take up approximately one page.
• Description of potential data sets to use for the experiments.
• Plan of activities, including what you plan to complete by the presentation date and how you plan to divide up the work.

The grading breakdown for the proposal is as follows:

• 40% for clear and concise description of proposed method
• 40% for literature survey that covers at least 4 relevant papers
• 10% for plan of activities
• 10% for quality of writing

The project proposal will be due at 11:59 PM on Thursday, October 20, and must be submitted via Gradescope.

Final Report

Your final report is expected to be 8 pages excluding references, in accordance with the length requirements for a CVPR paper. It should have roughly the following format:

• Introduction: problem definition and motivation
• Background & Related Work: background info and literature survey
• Methods
• Overview of your proposed method
• Intuition on why should it be better than the state of the art
• Details of models and algorithms that you developed
• Experiments
• Details of the experiments and results
• Conclusion: discussion and future work

The grading breakdown for the final report is as follows:

• 10% for introduction and literature survey
• 30% for proposed method (soundness and originality)
• 30% for correctness, completeness, and difficulty of experiments and figures
• 10% for empirical and theoretical analysis of results and methods
• 20% for quality of writing (clarity, organization, flow, etc.)

The project final report will be due at 11:59 PM on Friday, December 18, and must be submitted via Gradescope.

Note that late days do not apply to the final report.

Presentation

All project teams will present their work at the end of the semester. We will have a 2-3 hour long poster session. Each team should prepare a poster (similar in style to a conference poster) and present it during the allocated time. If applicable, live demonstrations of your software are highly encouraged.

Project Suggestions

You are encouraged to propose your own topics – some of you already may have done so. Take a look at the the resources listed at the end of this page for potential topics. Below are some ideas:

• Take a look at the latest papers from CVPR, ECCV, ICCV, NeurIPS, and ICML to find topics, software, datasets which you can build upon.
• Also check out the workshops associated with these conferences. For example, take a look at the recent Fine-grained Visual Recognition workshops for datasets and Kaggle challenges related to fine-grained classifiction tasks such as recognizing animal species, or product images.
• The website https://paperswithcode.com tracks the state of the art across datasets. This is a quick way to find baselines to compare with or build upon.
• The wesbite https://registry.opendata.aws contains a number of publicly available datasets hosted on AWS. These include satellite imagery, RADAR and other data on which you can try out some computer vision techniques. For example see MistNet and RoostNet projects (contact Zezhou for details).
• Explore the use of computer vision services on the cloud to solve some challenging problems. Some choices are AWS Rekognition, Google cloud, and Microsoft Azure.
• Generative modeling: Train and generate data on novel domains using GAN, VQ-GAN, Diffusion models. Build an interface for interactively edit images.
• Probing and understanding language and vision models: Models such as CLIP (OpenAI) and ALIGN (Google) train multi-modal aligned representations of images and text, which can be applied for example to retrive images based on text, or even generate images based on language (e.g., DALL-E). Huggingface has open-source implementation of many of these models – try to probe these models on new datasets to understand their capabilities and biases.

A sample of projects from a prior offering of the course:

• Scene text recognition
• Improving object detection using depth estimation
• Dust removal from images
• Fast face-retrieval using vocabulary trees on deep features
• Hyperspectral image classification
• Character recognition in movies
• Could motion analysis
• Analysis of medical images
• Stereo reconstruction survey
• UMass is an “AWS member institution”, so you are in the higher allowance tier. Use your .edu email and the full school name “University of Massachusetts Amherst” when you register to get the full benefits (a total of $100 annually). • To get GPUs, use g3 (up to 4 NVIDIA Tesla M60 GPUs) or p2 (up to 16 NVIDIA K80 GPUs) instances in EC2. Check the pricing first and make your plan accordingly! • Google Cloud Platform: https://cloud.google.com • You get$300 credits for the first 12 months, and always free on their free-tier resources (not including GPUs)