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/Sixkplus/TapLab
공식 코드TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge
https://github.com/Storife/RANet
공식 코드RANet: Ranking Attention Network for Fast Video Object Segmentation
https://github.com/TonyLianLong/RCF-UnsupVideoSeg
공식 코드Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
https://github.com/Va6lue/BST-Badminton-Stroke-type-Transformer
공식 코드BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
https://github.com/VisionTao/UOVOS
공식 코드Unsupervised Online Video Object Segmentation with Motion Property Understanding
https://github.com/Vujas-Eteph/CiVOS
공식 코드Revisiting Click-based Interactive Video Object Segmentation
https://github.com/Vujas-Eteph/READMem
공식 코드https://github.com/YeLyuUT/MSDNet
공식 코드https://github.com/YingjieYin/Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation
공식 코드Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation
https://github.com/YorkUCVIL/Static-Dynamic-Interpretability
공식 코드https://github.com/ZENGXH/DMM_Net
공식 코드DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
https://github.com/ZhilingYan/Biomedical-SAM-2
공식 코드Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
https://github.com/aim-uofa/omni-r1
공식 코드Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
https://github.com/ais-bonn/nimbro_api
공식 코드Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and Tracking
https://github.com/ait-assistive-autonomous-systems/hopomop
공식 코드https://github.com/akash2907/semi-sup-active-learning
공식 코드https://github.com/akash2907/stable_mean_teacher
공식 코드Stable Mean Teacher for Semi-supervised Video Action Detection
https://github.com/alanlukezic/d3s
공식 코드https://github.com/ali2500/burst-benchmark
공식 코드BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
https://github.com/alibaba/uvosam
공식 코드UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
https://github.com/alimohammadiamirhossein/smite
공식 코드https://github.com/alipga/AMM_VOS
공식 코드https://github.com/alxead/sfc
공식 코드https://github.com/amazon-science/polygon-transformer
공식 코드PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
https://github.com/amir4g/clvos23
공식 코드CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
https://github.com/amirbar/stop
공식 코드Stochastic positional embeddings improve masked image modeling
https://github.com/amirrezahmi/video-inpainting-and-voice-cloning
공식 코드https://github.com/amshaker/mavos
공식 코드Efficient Video Object Segmentation via Modulated Cross-Attention Memory
https://github.com/andr345/frtm-vos
공식 코드Learning Fast and Robust Target Models for Video Object Segmentation
https://github.com/andy-cheng/tempura
공식 코드TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action
https://github.com/angeloucn/min_max_similarity
공식 코드https://github.com/angelvillar96/MCDS-VSS
공식 코드https://github.com/appletea233/al-ref-sam2
공식 코드https://github.com/ashesham/tv3s
공식 코드Exploiting Temporal State Space Sharing for Video Semantic Segmentation
https://github.com/aspirinone/catr.github.io
공식 코드CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.