CVPR 2020 Tutorial on

Visual Recognition for Images, Video, and 3D

Location: Online

June 15th (full day), 2020


Nikhila Ravi


The purpose of this tutorial is to discuss popular approaches and recent advancements in the family of visual recognition tasks for different input modalities. We will cover in detail the most recent work on object recognition and scene understanding. Going beyond single images we will show current progress in video (detection and classification in video) and 3D visual recognition (multi-object mesh prediction). Our goal is to show existing connections between the techniques specialized for different input modalities and provide some insights about diverse challenges that each modality presents.

In conjunction with the tutorial we are open-sourcing three new visual recognition systems for images, videos, and 3D respectively. These PyTorch-based systems contain multiple state-of-the-art methods in the corresponding domains. In our tutorial we will pair each research talk with a talk that discusses these codebases sharing best engineering practices and showing details of implementation for each domain. We hope that such pairing will help researchers who are interested primarily in visual recognition to build and benchmark their systems easier. For researchers from different areas we hope to make SOTA recognition systems easy to incorporate in their frameworks.

Tutorial Videos

Session1: 2D Recognition

Live Q&A: 3:00 PM - 3:15 PM PDT
Zoom Link (password:faircvpr)

Ross Girshick - Object Detection as a Machine Learning Problem


Alexander Kirillov - Pixel-Level Recognition


Yuxin Wu - Detectron2


Session2: 3D Vision

Live Q&A: 3:20 PM - 3:35 PM PDT
Zoom Link (password:faircvpr)

Justin Johnson - Making 3D Predictions with 2D Supervision


Nikhila Ravi - PyTorch3D


Session3: Video Recognition

Live Q&A: 3:40 PM - 3:55 PM PDT
Zoom Link (password:faircvpr)

Christoph Feichtenhofer - Efficient Video Recognition


Haoqi Fan - PySlowFast


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