SOTAVerified

Multi-Object Tracking and Segmentation

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes.

(Image and definition credit: Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021, Spotlight )

Papers

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Showing 1–10 of 28 papers

TitleStatusHype
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation—0
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation—0
Deep Learning Techniques for Video Instance Segmentation: A Survey—0
Universal Instance Perception as Object Discovery and RetrievalCode3
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic SegmentationCode1
Track Targets by Dense Spatio-Temporal Position Encoding—0
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in VideoCode1
Multi-Object Tracking and Segmentation via Neural Message PassingCode1
Towards Grand Unification of Object TrackingCode2
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
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All datasetsBDD100K valKITTI MOTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EagerMOTAssA73.75—Unverified