Papers with Code 아카이브
Video Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jqueguiner/camembert-as-a-service
https://github.com/jqueguiner/image-segmentation
https://github.com/junbongjang/contour-tracking
https://github.com/justld/CCNet_paddle
https://github.com/kai422/iart
Accelerating Video Object Segmentation with Compressed Video
https://github.com/kannyjyk/Nested-UNet
https://github.com/kazuto1011/pspnet-pytorch
https://github.com/kdhingra307/temp
https://github.com/kevinddchen/Keras-FCN
https://github.com/kim-younghan/FEELVOS
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
https://github.com/kingcong/PSPNet
https://github.com/kini5gowda/ALBA-RL-for-VOS
https://github.com/kuangzijian/Flow-Based-Video-Matting
https://github.com/kukby/Mish-semantic-segmentation-pytorch
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/lojzezust/lars_evaluator
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
https://github.com/louisfinner/him2sam
https://github.com/lxx1991/VS-ReID
https://github.com/manideep2510/eye-in-the-sky
https://github.com/mattangus/fast-semantic-segmentation
https://github.com/mayu-ot/rethinking-evs
https://github.com/mdt48/semantic-segmentation-pytorch
https://github.com/mindspore-ai/models/tree/master/official/cv/FCN8s
https://github.com/mindspore-ai/models/tree/master/research/cv/PSPNet
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CrissCrossAttention.py
https://github.com/mkimhi/RISE
https://github.com/nigelyaoj/self-supervised-amodal-video-object-segmentation
https://github.com/nihalsid/mask2former
https://github.com/oandrienko/fast-semantic-segmentation
https://github.com/omkar13/MaskTrack
https://github.com/open-mmlab/mmaction2
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmsegmentation
https://github.com/open-mmlab/mmtracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.