16-824 Visual Learning and Recognition

12 units, Carnegie Mellon University

This graduate-level computer vision course explores representation and reasoning for large-scale data, such as images, videos, 3D data, and text, toward understanding the visual world surrounding us. Students will engage with a diverse selection of classic and recent research papers covering mid-level vision (grouping, segmentation), object and scene recognition, 3D scene understanding, action recognition, multimodal perception, vision-language models, multimodal deep generative models, efficient deep learning, and more. We will explore state-of-the-art neural architectures, including CNNs and transformers, and a wide range of supervised, semi-supervised, self-supervised, and unsupervised approaches for each topic above.

Prerequisites: 10-701 (min B) or 16-722 (min B) or 16-385 (min B) or 15-781 (min B) or 16-720 (min B)

Offered: Spring 2027, Fall 2026, Fall 2025

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