Video Instance Segmentation
85 papers with code • 8 benchmarks • 8 datasets
The goal of video instance segmentation is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain.
To facilitate research on this new task, a large-scale benchmark called YouTube-VIS, which consists of 2,883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks is built.
Benchmarks
These leaderboards are used to track progress in Video Instance Segmentation
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Libraries
Use these libraries to find Video Instance Segmentation models and implementationsDatasets
BDD100K
YouTube-VIS 2019
OVIS
YouTube-VIS 2021
UVO
BURST
Youtube-VIS 2022 Validation
HQ-YTVIS
Most implemented papers
Simple Online and Realtime Tracking with a Deep Association Metric
nwojke/deep_sort
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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
Video Instance Segmentation
The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos.
Instances as Queries
hustvl/QueryInst
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ICCV 2021
The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage.
Mask2Former for Video Instance Segmentation
facebookresearch/Mask2Former
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We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
Temporally Efficient Vision Transformer for Video Instance Segmentation
To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).
End-to-End Video Instance Segmentation with Transformers
Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem.
Occluded Video Instance Segmentation: A Benchmark
On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
wvangansbeke/Revisiting-Contrastive-SSL
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NeurIPS 2021
Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection.
UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution
In this report, we introduce our (pretty straightforard) two-step "detect-then-match" video instance segmentation method.
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
schmiddo/d2conv3d
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WACV 2021
We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
