Papers with Code 아카이브
Video Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/wkentaro/pytorch-fcn
https://github.com/wudao001/LSMVOS
LSMVOS: Long-Short-Term Similarity Matching for Video Object
https://github.com/xiaolonw/TimeCycle
https://github.com/xilin1991/ClusterNet
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
https://github.com/xmed-lab/cardiacnet
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmed-lab/m3-uda
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xujxyang/opentrans
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
https://github.com/y-ouali/pytorch_segmentation
https://github.com/yangchris11/samurai
https://github.com/yangyucheng000/PSPNet
https://github.com/yangyucheng000/University/tree/main/model-3/sam
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
https://github.com/yangyucheng000/University/tree/main/model-3/seggpt
https://github.com/yelantingfeng/pyLucid
https://github.com/yformer/EfficientSAM
https://github.com/youngsean/unseenobjectswithmeanshift
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
https://github.com/ywcmaike/TianchiVideoCharacterSegmentationPreliminary
https://github.com/zhang-tao-whu/DVIS
DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
https://github.com/zixuan-chen/M3VOS_Experiment
M$^3$-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation
https://github.com/zj603252818/FCN8s
https://github.com/zzy-zhou/renet
Event-Free Moving Object Segmentation from Moving Ego Vehicle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.