• Welcome to CS223C: The Cutting Edge of Computer Vision.

• We use Piazzza, an online collaboration forum during the quarter. Please sign up and enroll in "CS 223C SPRING 2011: The Cutting Edge of Computer Vision" on it. Feel free to ask any course related question there.

• Course staff email "cs223c-spr1011-staff [at] lists [dot] stanford [dot] edu".

• Please email course staff your code and write-up at ""

• Prof. Li's May 11 lecture has been changed to Gates 2A Open Space (219A), at 9:30-10:45am, Thursday May 12.

NEW: The deadline for project 3 has been extended to Tuesday, May 31 23:59pm.

NEW: The schedule for May 23 and May 25 has been swapped. Nikil and Salik will present on Monday May 23, while Dr. Yu will present on Wednesday May 25.



Instructor: Prof. Fei-Fei Li

Office: Room 246 Gates Bldg

Phone: (650)725-3860

Email: feifeili [at] cs [dot] stanford [dot] edu

Office hours: 11:00am-12:00pm, Mondays and Wednesdays, Gates 246


Assistant Instructor: Bangpeng Yao

Office: Room 241 Gates Bldg

Email: bangpeng [at] cs.stanford [dot] edu

Office hours: 4:00pm-5:00pm, Tuesdays and Fridays, Gates B26A


Class Time and Location:

Monday & Wednesday, 9:30am-10:45am, Hewlett 103

Course Description:

More than one-third of the brain is engaged in visual processing, the most sophisticated human sensory system. Yet visual recognition technology has fundamentally influenced our lives on the same scale and scope as text-based technology has, thanks to Google, Twitter, Facebook, etc.
This course is designed for those students who are interested in cutting edge computer vision research, and/or are aspiring to be an entrepreneur using vision technology. During the 10-week course, we will guide the students through the design and implenentation of three core vision technologies: segmentation, detection and classification on three highly practical, real-world problems. We will focus on teaching the fundamental theory, detailed algorithms, practical engineering insights, and guide them to develop state-of-the-art systems evaluated based on the most modern and standard benchmark datasets.

Grading policy:

  Paper presentation and participation: 15%
  Course projects (including code, write up, presentation): 85%
     -   Project 1: 25%;   Project 2: 25%;   Project 3: 35%



CS223B or equivalent (need instructor's approval), and a good machine learning background (e.g. CS221, CS228, CS229).
Coding skills: fluent in Matlab, C/C++.


None required.