No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
NVIDIA GTC San Jose 2017

S7783 - A Fast, Unified Method for Object Detection, Instance Segmentation, and Human Pose Estimation

Session Speakers
Session Description

We'll cover state-of-the-art algorithms for image classification, object detection, object instance segmentation, and human pose prediction that we recently developed at Facebook AI Research. Our image classification results are based on the recently developed "ResNeXt" model that supersedes ResNet's accuracy on ImageNet, but much more importantly yields better features with stronger generalization performance on object detection tasks. Using ResNeXt as a backbone, we'll present a unified approach for detailed object instance recognition tasks, such as instance segmentation and human pose estimation. This model builds on our prior work on the Faster R-CNN system with Feature Pyramid Networks, which enables efficient multiscale recognition. We'll describe our platform for object detection research that enables a fast and flexible research cycle. Our platform is implemented on Caffe2 and can train many of these state-of-the-art models on the COCO dataset in 1-2 days using sync SGD over eight GPUs on a single Big Sur server.


Additional Session Information
Intermediate
Talk
Algorithms, Computer Vision and Machine Vision, Deep Learning and AI
Other
25 minutes
Session Schedule