(16-824 Spring 2017) Visual Learning and Recognition


Where: GHC 4307
When: Monday/Wednesday 1:30 PM - 2:50 PM
Instructors: Abhinav Gupta
Office Hours: By appointment

<CVPR VU Challenge for Class Projects>

<Piazza for discussions>



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.

Awards: At the end of the course, we will have prizes for:

  • Best blog posts (2x)

  • Best Proposal

  • Best Project


Abhinav Gupta Lerrel Pinto Senthil Purushwalkam
EDSH 213EDSH 101EDSH 235

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