Joint Recognition Challenge Workshop at ECCV 2020

Table of Contents

  1. Workshop Schedule
  2. Overview
  3. Dates
  4. Organizers
  5. Rules and Awards
  6. Challenge tracks

1. Workshop Schedule - August 23, 2020

Note: the time zone is UTC+1.
We will host two live sessions with panel discussion during the second one.

0:00am and 4:00pm COCO introduction and results [Talk]
0:15am and 4:15pm COCO keypoints winner [Talk] Team XForwardAI
0:30am and 4:30pm LVIS introduction and results [Talk] Agrim Gupta
0:40am and 4:40pm LVIS winner [Talk] Team LvisTraveler
0:55am and 4:55pm LVIS most innovative [Talk] Team Asynchronous SSL
1:10am and 5:10pm LVIS spotlight [Talk][Video] Team MMDet
1:15am and 5:15pm LVIS spotlight [Talk][Video] Team CenterNet2
1:20am and 5:20pm LVIS spotlight [Talk][Video] Team PAI Vision
1:25am and 5:25pm LVIS spotlight [Talk][Video] Team Innova
5:30pm Panel: The future of computer vision datasets, benchmarks, and challenges Award committee

2. Overview

The goal of the joint COCO and LVIS Workshop is to study object recognition in the context of scene understanding. This workshop will host the COCO suite of challenges and a new challenge on large vocabulary instance segmentation (LVIS). While both the COCO and LVIS challenges look at the general problem of visual recognition, the specific tasks in the challenges probe different aspects of the problem.

COCO is a widely used visual recognition dataset, designed to spur object detection research with a focus on full scene understanding. In particular: detecting non-iconic views of objects, localizing objects in images with pixel level precision, and detection of objects in complex scenes. The COCO dataset includes 330K images of complex scenes exhaustively annotated with 80 object categories with segmentation masks, 91 stuff categories with segmentation masks, person keypoint annotations, and 5 captions per image.

Large Vocabulary Instance Segmentation (LVIS) includes high-quality instance segmentations for more than 1000 entry-level object categories. The LVIS dataset contains a long-tail of categories with few examples, making it a distinct challenge from COCO and exposes shortcomings and new opportunities in machine learning. We expect this dataset to inspire new methods in the detection research community. This year we plan to host the first challenge for LVIS, a new large vocabulary dataset.

3. Challenge Dates

August 7, 2020
Submission deadline (11:59 PM PST) (1 week extension)
August 10, 2020
Technical report submission deadline (11:59 PM PST)
August 17, 2020
Challenge winners notified
August 21, 2020
Presenter's slides and videos due (submitted to organizers)
August 23, 2020
ECCV 2020 Workshop

4. Organizers

4.1. COCO

4.2. LVIS

4.3. Award committee

5. Rules and Awards

6. COCO Challenges

COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image. The specific tracks in the COCO 2018 Challenges are (1) object detection with segmentation masks (instance segmentation), (2) panoptic segmentation, (3) person keypoint estimation, and (4) DensePose. We describe each next. Note: neither object detection with bounding-box outputs nor stuff segmentation will be featured at the COCO 2020 challenge (but evaluation servers for both tasks remain open).

6.1. COCO Object Detection Task

The COCO Object Detection Task is designed to push the state of the art in object detection forward. Note: only the detection task with object segmentation output (that is, instance segmentation) will be featured at the COCO 2019 challenge. For full details of this task please see the COCO Object Detection Task.

6.2. COCO Panoptic Segmentation Task

The COCO Panoptic Segmentation Task has the goal of advancing the state of the art in scene segmentation. Panoptic segmentation addresses both stuff and thing classes, unifying the typically distinct semantic and instance segmentation tasks. For full details of this task please see the COCO Panoptic Segmentation Task.

6.3. COCO Keypoint Detection Task

The COCO Keypoint Detection Task requires localization of person keypoints in challenging, uncontrolled conditions. The keypoint task 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 Keypoint Detection Task.

6.4. COCO DensePose Task

The COCO DensePose Task requires dense estimation of human pose in challenging, uncontrolled conditions. The DensePose task involves simultaneously detecting people, segmenting their bodies and mapping all image pixels that belong to a human body to the 3D surface of the body. For full details of this task please see the COCO DensePose Task.

7. LVIS Challenge

LVIS is a new, large-scale instance segmentation dataset that features > 1000 object categories, many of which have very few training examples. LVIS presents a novel low-shot object detection challenge to encourage new research in object detection. For more information, please see LVIS challenge page.