16-824: Visual Learning and Recognition

Spring 2021

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Course Overview

Summary: A graduate course in Computer Vision with emphasis on representation and reasoning for large amounts of data (images, videos and associated tags, text, gps-locations etc) toward the ultimate goal of Image Understanding. We will be reading an eclectic mix of classic and recent papers on topics including: Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poselets), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Image Parsing, Joint Language and Vision Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.

Prerequisites: While there are no formal prerequisites, this course assumes familiarity with computer vision (16-720 or similar) and machine learning (10-601 or similar). If you have not taken courses covering this material, consult with the instructor.

Course Information





Students will be expected to:

Grading Policy

Collaboration Policy

For each assignment, TAs will not be looking over any of your code before the assignment deadline. You may discuss code with classmates. Please list collaborators whom you discussed with in the assignment write-up.

Late Policy Policy

You have 7 late days which you can use over the course of the semester. Any assignment submitted after all 7 late days are used will receive 0 credit.


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