Contact

Mission

• Leading the rapid development of AI technology to become a global expert and address various practical problems.

• We are looking for self-motivated graduate & undergraduate students. If you're interested, send an email to [jihyongoh@cau.ac.kr] with the following information: 

(1) full Curriculum Vitae (CV), (2) transcript (3) future plan & purpose, and (4) available participation time. 

Application for Graduate School

• We aim to cultivate excellent expertise in AI-based Computer Vision & Multimedia and practice through engaging in research, industry-university projects, studying on vacation, and participating in AI top-tier conferences including challenge competitions.

• If you are interested in pursuing graduate school, we recommend applying for lab internships at least 3~6 months in advance.

• Please note that becoming an expert in the AI research field typically requires 5 years for integrated PhD programs, 4 years for doctoral programs, and 2 years for master's programs.

• For students enrolled in integrated PhD programs or doctoral programs, we strongly encourage participating in AI top-tier conferences, presenting papers, and undertaking internships at domestic & global companies during the degree program.

Undergraduate Research Program

• It is designed for undergraduate students who have a keen interest in pursuing graduate courses in the field of AI-based Computer Vision & Multimedia. It offers study and lab-independent education programs similar to those available to graduate students.

• If a seat is available in the lab, selected undergraduate students will be provided with a dedicated workspace for the duration of their participation. The priority is how well you know the below Prerequisites (excluding "Advanced").

• Furthermore, if a graduate student is working on a topic of interest within the lab, the undergraduate research student may engage in mentoring and research activities in consultation with the graduate student.

Prerequisites (Recommended: please be aware before contacting)

Tips: [Googling!] If you encounter a concept or term that you are unfamiliar with, make sure to actively search for it on Google. There is no information that cannot be found through Google 🤩.

• Coding Skills (very important)

  • It is important to quickly & efficiently implement your ideas to boost your AI research! All global & domestic tech companies seek to hire AI Research Scientists or Software Engineers who possess fluent coding skills with high standards. 

1. [Basic Programming Language] Python

•  (i) Basic Document with Practice Terminal ---> (ii) Numpy Tutorial ---> (iii) AI-tech-interview

2. [Deep Learning Libraries] Select either PyTorch (Part1~3, more recommended) or TensorFlow (Part1~3)

3. [Basic OS] Ubuntu

• • • • [AI Research Tool] Anaconda + VSCode (source-code editor, more recommended) or PyCharm (source-code editor) + GitHub (for managing coding history & sharing)

      • [GPU] NVIDIA Driver + CUDA + CuDNN

4. [Advanced] Preparation for internship or full-time positions for tech companies.

• • • • (i) Data Structure & Algorithms ---> (ii) Leetcode (Easy ~ Medium levels are enough.)

• Machine Learning 

1. CS229 (L1 ~ 10) ---> AI-tech-interview

2. Statistics

• Deep Learning

1. CS231n [Eng. (L1 ~ 13) or Kor. (All)]

• • • • Assignment#2

2. AI-tech-interview

3. [Advanced] Transformer for Computer Vision

   • CBAM, Self-Attention, Swin Transformer, SwinIR, MAXIM, MaxViT, etc.

• [Advanced] Introduction to Research Area (if you are further interested 😮)

  • • Low-Level Vision (LLV)

      • Image-related tasks: CBAM (channel attention), RDN, Swin Transformer, SwinIR, KOALAnet (dynamic filtering), LIIF (INR), MAXIM & MaxViT (linear complexity of transformer), Restormer, GRL, etc.

      • Video (motion handling)-related tasks: BasicVSR, BaiscVSR++ (important for overview), RAFT (basic OF), ABME (basic VFI), XVFI (backward warping, 4K), Softsplat (forward warping), RIFE, M2M, VFIformer, Shift-Net, FlowFormer++,  AnyFlow, MoTIF, etc. 

      • Diffusion-based LLCV tasks: CVPR2023 Tutorial, survey paper, DiffIR, TIP,  SupIR etc.

    • Neural Radiance Fields (NeRFs)

      • [Prerequisites] Computer Graphics, Camera Calibration, Radiometry & Reflectance, etc.

      • [Tutorial]  Tutorial on NeRFs at ECCV 2022 by Prof. Angjoo Kanazawa.

      • [Introduction Paper] Nerfstudio [SIGGRAPH2023], 3D Gaussian Splatting [SIGGRAPH 2023]

      • [Coding Tool] (i) Nerfstudio [by UC Berkeley], (ii) nerf-pytorch [by Yen-Chen Lin], (iii) hyperreel [by Meta], etc.