DLCV - VISION & LEARNING LAB

Introduction

Computer Vision has become ubiquitous in our society, with applications in image/video search and understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, segmentation, localization and detection. Recent developments in neural network (a.k.a. deep learning) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for solving these tasks.

Goals

This course will expose students to cutting-edge research — starting from a refresher in basics of machine learning, computer vision, neural networks, to recent developments. The emphasis will be on student-led paper presentations and discussions. Each topic will begin with instructor lectures to present context and background material.

Syllabus

Week	Date	Topic	Course Materials	Remarks
0	02/20	Course Logistics + Intro to Computer Vision	[Slides]
1	02/27	Machine Learning 101	[Slides]
2	03/06	Image Representation: From Recognition to Tracking	[Slides]	HW #1 out
3	03/13	Intro to Neural Networks + CNN (I)	[Slides]
4	03/20	Intro to Neural Networks + CNN (II) Tutorial on Python, PyTorch, GitHub, etc.	[Slides]	HW #1 due
5	03/27	Detection & Segmentation		HW #2 out
6	04/03	Spring Break
7	04/10	Generative Models		[]
8	04/17	Visualization and Understanding NNs		HW #3 out, HW #2 due
9	04/24	Transfer Learning for Visual Analysis
10	05/01	Recurrent NNs and Seq-to-Seq Models (I)		Team Up for Final Project
11	05/08	Guest Lecture (ICLR week)		HW #4 out, HW #3 due
12	05/15	Recurrent NNs and Seq-to-Seq Models (II)
13	05/22	Learning Beyond Images (2D/3D, depth, etc.)
14	05/29	Deep Reinforcement Learning for Visual Applications		HW #4 due
15	06/05	Final Project Checkpoint	[]
16	06/12	Final Presentation	[]	[]