Occluded Video Instance Segmentation (OVIS)

A workshop and challenge focusing on occluded object understanding in video

October 11th - 17th, ICCV 2021 Workshop

Challenge Track

In the visual world, objects rarely occur in isolation. The psychophysical and computational studies have demonstrated that human vision systems can perceive heavily occluded objects with contextual reasoning and association. The question then becomes, can our video understanding system perceive objects that are severely obscured? The OVIS competition will be hosted on an online platform and presentations will be delivered on Zoom.

Dataset
OVIS is a new large scale benchmark dataset for video instance segmentation task. It is designed with the philosophy of perceiving object occlusions in videos, which could reveal the complexity and the diversity of real-world scenes. OVIS consists of:
  • 296k high-quality instance masks
  • 25 commonly seen semantic categories
  • 901 videos with severe object occlusions
  • 5,223 unique instances

We use average precision (AP) at different intersection-over-union (IoU) thresholds and average recall (AR) as our evaluation metrics, following Youtube-VIS. The IoU in video instance segmentation is the sum of intersection area over the sum of union area across the video.

Dataset Download
Evaluation Server
For more details about the dataset, please refer to our paper or website.
Competition Schedule
Competition Date
Competition Phase 1 (open the submission of the val results) June 1, 2021 (11:59PM Pacific Time)
Competition Phase 2 (open the submission of the test results) July 25, 2021 (11:59PM Pacific Time)
Deadline for Submitting the Final Predictions August 1, 2021 (11:59PM Pacific Time)
Decisions to Participants August 6, 2021 (11:59PM Pacific Time)

Paper Track

Call for Papers

Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving objects in heavy occlusion video scenes is still a very challenging task. The difficulty of precisely localizing and reasoning heavily occluded objects in videos reveals that current deep learning models perform differently with the human vision system, and confirms that it is urgent to design new paradigms for video understanding.

Topics include but not limited to:
  • Video understanding
  • Occluded video instance segmentation
  • Occluded object detection
  • Occlusion reasoning
  • Occlusion edge detection
  • Video object segmentation
  • Video object detection
  • Multi-object tracking
Submitted papers must follow the ICCV 2021 paper template and should be from 4 to 8 pages in length (excluding citations). The review process will be double-blind and submissions must be anonymized. All accepted papers will be published in IEEE ICCVW proceedings. Both challenge and regular papers are welcome. (You can submit your paper without participating in our challenge.) Please submit online via CMT Website.
Paper Track Schedule
Paper Date
Submission Deadline July 25, 2021 (11:59PM Pacific Time)
Author Notification August 6, 2021 (11:59PM Pacific Time)
Camera-ready Due August 13, 2021 (11:59PM Pacific Time)

ICCV 2021 Workshop

Confimred Speakers


Workshop Schedule
Date Speaker Topic
9:00-9:15 Organizers Welcome & introduction
9:15-9:45 Prof. Alan Yuille Invited talks topic 1
9:45-10:00 OVIS 3rd place team TBA
10:00-10:15 OVIS 2nd place team TBA
10:15-10:45 - Poster section & Coffee break
10:45-11:00 OVIS 1st place team TBA
11:00-11:30 TBA Oral section
11:30-12:00 Prof. Hanwang Zhang Invited talks topic 2

Organizers