Group Project (Semester A 2025/2026)

⭐ Create Your Own Data and use an existing approach

The main focus is on collecting data and making it useful for existing DL methods. A good example is that you collect some images and annotate them, and then train an existing image classifier to perform a specific task on these images.

⭐⭐ Create Your Own Method on an existing dataset

Taking an existing dataset and adapting an existing method to make it your own DL method. You modify parameters, work with existing neural networks, apply what you've learned in the lecture, and aim to make them more efficient.

⭐⭐⭐ Beat the Classics - Implement a DL method and compare to non-DL baseline

Challenge the state-of-the-art using algorithms without DL or with DL algorithms that aim to surpass this baseline. Compare the results and demonstrate the ability to outperform the baseline, referred to as "beat".

⭐⭐⭐ Create Your Own AI-Powered Application

Create a practical AI-powered tool or service addressing a real-world problem using advanced AI and deep learning technologies. Demonstrate creativity and technical proficiency in a functional solution.

⭐⭐⭐⭐ Beat the Stars - Improve the state of the art

By selecting a research paper, the goal is to demonstrate how to outperform the current state-of-the-art papers. It's important to note that due to the rapid pace of paper publication, it might be difficult to stay up-to-date with the latest advancements. Nonetheless, the objective is to select a recent paper as a baseline and attempt to beat it.

Project Team Formation (Week 3)

• Create a team of 5 people and choose a team leader
• Team leader is responsible for submitting all group project materials
• Send team member list and elected leader to: eelmpo@cityu.edu.hk

Project Proposal (Week 4)

A 5-page project proposal (not including references) submitted in PDF format to CANVAS.

• Project Title
• Student Name, Student ID and Email Address of each member
• Summary with goals (300 words)
• Problem statement and objectives
• Methodology and dataset description
• Baseline selection and experimental setup
• Timeline and collaboration plan
• References

Oral Presentation (Week 12 and 13)

10-minute PowerPoint presentation to the entire class assessing communication skills and technical understanding.

• Short description of project and objectives
• Explanation of implemented algorithm and relevant theory
• Demonstration of working program with results

• Upscaling Images with Neural Networks by Geoffrey Litt
• This is a great technical presentation for students to learn about industry presentation styles.
• YOLO Presentation at CVPR2016

Final Technical Report and Deliverables (Week 14)

Minimum 25-page final report following technical report structure.

• Final technical report (PDF) - 25+ pages minimum
• PowerPoint presentation file
• Python source code
• 3-4 minute demo video summary
• Appendix A: Individual Contributions Report

Template Technical Report: Use this technical report template with LaTex. Must include Appendix A for individual contributions assessment.