The goal of the joint COCO and Places Challenge is to study object recognition in the context of scene understanding.
2. COCO Challenges
COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include pixel-level segmentation of object belonging to 80 categories, keypoint annotations for person instances, stuff segmentations for 91 categories, and five image captions per image. The specific tracks in the COCO 2017 Challenges are (1) object detection with bounding boxes and segmentation masks, (2) joint detection and person keypoint estimation, and (3) stuff segmentation. We describe each next.
2.1. COCO Detection Challenge
The COCO 2017 Detection Challenge is designed to push the state of the art in object detection forward. Teams are encouraged to compete in either (or both) of two object detection challenges: using bounding box output or object segmentation output. For full details of this task please see the COCO Detection Challenge page.
2.2. COCO Keypoint Challenge
The COCO 2017 Keypoint Challenge requires localization of person keypoints in challenging, uncontrolled conditions. The keypoint challenge involves simultaneously detecting people and localizing their keypoints (person locations are not given at test time). For full details of this task please see the COCO Keypoints Challenge page.
2.3. COCO Stuff Challenge
The COCO 2017 Stuff Segmentation Challenge is designed to push the state of the art in semantic segmentation of stuff classes. Whereas the COCO 2017 Detection Challenge addresses thing classes (person, car, elephant), this challenge focuses on stuff classes (grass, wall, sky). For full details of this task please see the COCO Stuff Challenge page.
3. Places Challenges
The Places Challenge will host three tracks meant to complement the COCO Challenges. The data for the 2017 Places Challenge is from the pixel-wise annotated image dataset ADE20K, in which there are 20K images for training, 2K validation images, and 3K testing images. The three specific tracks in the Places Challenge 2017 are: (1) scene parsing, (2) instance segmentation, and (3) semantic boundary detection. See the Places Challenge Page for detailed information.
4. Challenge Dates
5. Workshop Schedule - 10.29.2017
|9:00||Detection Challenge Track||Tsung-Yi Lin (Cornell Tech, Google Research)|
|9:10||Detection/Segmentation and Places Challenge Competitor||Team Megvii (Face++)|
|9:30||Detection/Segmentation Challenge Competitor||Team UCenter (CUHK & Peking University)|
|9:50||Detection/Segmentation Challenge Competitor||Team MSRA|
|10:00||Detection/Segmentation Challenge Competitor||Team FAIR|
|10:10||Morning Break: Coffee + Posters|
|10:40||Keypoints Challenge Track||Matteo Ronchi (Caltech)|
|10:50||Keypoints Challenge Competitor||Team Megvii (Face++)|
|11:05||Keypoints Challenge Competitor||Team OKS (Beihang University & SenseTime)|
|11:20||Stuff Challenge Track||Holger Caesar (University of Edinburgh)|
|11:30||Stuff Challenge Competitor||Team FAIR|
|11:45||Stuff Challenge Competitor||Team Oxford|
|12:00||Invited Talk||Vladlen Koltun (Intel Labs)|
|14:00||Invited Talk||Raquel Urtasun (University of Toronto, Vector Institute, Uber ATG Toronto)|
|14:30||Places Challenge Track||Bolei Zhou (MIT), Hang Zhao (MIT)|
|14:40||Places Challenge Competitor||Team CASIA_IVA_JD (Institute of Automation, Chinese Academy of Sciences, & JD)|
|15:00||Places Challenge Competitor||Team WinterIsComing (ByteDance)|
|15:20||Places Challenge Competitor||Team G-RMI (Google Research)|
|15:45||Afternoon Break: Coffee + Posters|
|16:15||Discussion Panel||Genevieve Patterson (MSR NE), Hang Zhao (MIT),|
Note: schedule subject to change.
6. Invited Speakers
University of Toronto, Vector Institute, Uber ATG Toronto
Raquel Urtasun is the Head of Uber ATG Toronto. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI.
I direct a basic research lab at Intel Labs. We are based in two locations: Santa Clara, California and Munich, Germany. We are hiring interns, postdocs, and staff researchers in both locations. We are broadly interested in visual computing and intelligent systems. Our work is usually published in computer vision, machine learning, and computer graphics conferences.