AID is a university AI research club where students study artificial intelligence topics and apply them through study sessions, ideathons, and competitions.
As a member of the 6th cohort, I participated in regular computer vision study sessions and AI-based ideathon activities. I also extended what I learned from these activities to AI and computer vision competitions, including the 2024 PNU CSE Ideathon and the Ajou Deep Learning Challenge.
Through these experiences, I explored how AI models can be applied to real-world problems beyond simply learning algorithms.
Computer Vision Study
Through regular study sessions, I learned core computer vision concepts and explored how AI models can be applied to practical tasks.
The study helped me understand not only model performance, but also how data, problem definition, and evaluation methods affect AI-based solutions.
https://github.com/Neibce/AID-CV-DL-STUDY
AI-based Ideathon – 2024 PNU CSE Ideathon
I participated in the 2024 PNU CSE Ideathon as a team participant and practiced turning AI-based ideas into practical service proposals.
Through this activity, I learned how to connect technical ideas with real user problems, service scenarios, and presentation strategies. Our team received 3rd Place in the competition.
Individual CV Competition – Ajou Deep Learning Challenge
I participated individually in the 3rd Ajou Deep Learning Challenge and solved a zero-shot scene classification task using vision-language models.
The challenge required solving the problem without training or fine-tuning on the target-class dataset. I received 1st Place as an individual participant.
https://github.com/Neibce/Ajou-DL-Challenge-2024
