https://github.com/vvarga90/gnn_annot
공식 코드Fast Interactive Video Object Segmentation with Graph Neural Networks
Papers with Code 아카이브
Video Semantic Segmentation
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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/wangbo-zhao/2022cvpr-mmmmtbvs
공식 코드Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation
https://github.com/wangchi95/active-boundary-loss
공식 코드https://github.com/wanghao9610/TMANet
공식 코드https://github.com/weihaosky/CycleSiam
공식 코드Self-supervised Object Tracking with Cycle-consistent Siamese Networks
https://github.com/wenguanwang/vos_correspondence
공식 코드Boosting Video Object Segmentation via Space-time Correspondence Learning
https://github.com/wjf5203/vnext
공식 코드https://github.com/wjn922/referformer
공식 코드https://github.com/workforai/gsfm
공식 코드Global Spectral Filter Memory Network for Video Object Segmentation
https://github.com/workforai/qdmn
공식 코드Learning Quality-aware Dynamic Memory for Video Object Segmentation
https://github.com/wudongming97/onlinerefer
공식 코드OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
https://github.com/wuzijian1997/sis-pt-sam
공식 코드https://github.com/xiaoqi-zhao-dlut/multi-source-aps-zvos
공식 코드Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
https://github.com/xilin1991/CluterNet
공식 코드Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
https://github.com/xilin1991/IMCNet
공식 코드Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
https://github.com/xing0047/tps
공식 코드Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
https://github.com/xmed-lab/GL-Fusion
공식 코드GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
https://github.com/xmed-lab/GraphEcho
공식 코드GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmlyqing00/AFB-URR
공식 코드Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
https://github.com/xushilin1/rap-sam
공식 코드https://github.com/xvjiarui/VFS
공식 코드Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/yichizhang98/sam4mis
공식 코드Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey
https://github.com/yk-pku/two-shot-video-object-segmentation
공식 코드https://github.com/yoxu515/aot-benchmark
공식 코드Scalable Video Object Segmentation with Identification Mechanism
https://github.com/yoxu515/mits
공식 코드Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
https://github.com/yoxu515/viposeg-benchmark
공식 코드https://github.com/yujun0-0/mma-net
공식 코드VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
https://github.com/yuk6heo/GIS-RAmap
공식 코드Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
https://github.com/yuk6heo/IVOS-ATNet
공식 코드Interactive Video Object Segmentation Using Global and Local Transfer Modules
https://github.com/yunkezhang/TCVOM
공식 코드https://github.com/yunlong10/ads-1k
공식 코드Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward
https://github.com/yunongLiu1/IKEA-Manuals-at-Work
공식 코드IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos
https://github.com/yz93/anchor-diff-VOS
공식 코드https://github.com/z-x-yang/AOT
공식 코드Associating Objects with Transformers for Video Object Segmentation
https://github.com/z-x-yang/CFBI
공식 코드Collaborative Video Object Segmentation by Foreground-Background Integration
https://github.com/zamling/psalm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.