https://github.com/MasterBin-IIAU/UNINEXT
공식 코드Universal Instance Perception as Object Discovery and Retrieval
Papers with Code 아카이브
Multi-Object Tracking and Segmentation
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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 )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SonginCV/MAF_HDA
공식 코드Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
https://github.com/SysCV/bdd100k-models
공식 코드BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/SysCV/pcan
공식 코드Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
https://github.com/Zhongdao/UniTrack
공식 코드Do Different Tracking Tasks Require Different Appearance Models?
https://github.com/aleksandrkim61/EagerMOT
공식 코드https://github.com/ali2500/burst-benchmark
공식 코드BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
https://github.com/bdd100k/bdd100k
공식 코드BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/detectRecog/PointTrack
공식 코드Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
https://github.com/gafaua/polytrack
공식 코드https://github.com/masterbin-iiau/unicorn
공식 코드https://github.com/ocetintas/mpntrackseg
공식 코드Multi-Object Tracking and Segmentation via Neural Message Passing
https://gitee.com/gai-shaoyan/mind3d
https://github.com/JrkJamie/eagermot_ms
https://github.com/irh-ca-team-car/attention-data
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.