Video Instance Segmentation
43 papers with code • 4 benchmarks • 4 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 implementations
BDD100K
YouTubeVIS
OVIS
UVO
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.
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.
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.
Temporally Efficient Vision Transformer for Video Instance Segmentation
hustvl/tevit
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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).
Efficient Video Object Segmentation via Network Modulation
linjieyangsc/video_seg
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CVPR 2018
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
007: Democratically Finding The Cause of Packet Drops
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.
Instance-wise Depth and Motion Learning from Monocular Videos
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
jdc08161063/spatio-temporal-embedding
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We present a novel embedding approach for video instance segmentation.
