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/beileicui/ms-tfal
공식 코드https://github.com/bo-miao/HTR
공식 코드Temporally Consistent Referring Video Object Segmentation with Hybrid Memory
https://github.com/bo-miao/MAMP
공식 코드Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation
https://github.com/bo-miao/sgmg
공식 코드Spectrum-guided Multi-granularity Referring Video Object Segmentation
https://github.com/bowang-lab/medsam
공식 코드Segment Anything in Medical Images and Videos: Benchmark and Deployment
https://github.com/bowang-lab/medsam2
공식 코드https://github.com/buaahugegun/dcfm
공식 코드Deep Common Feature Mining for Efficient Video Semantic Segmentation
https://github.com/buxiangzhiren/vd-it
공식 코드Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation
https://github.com/caganselim/tltm
공식 코드Two-Level Temporal Relation Model for Online Video Instance Segmentation
https://github.com/carrierlxk/AGNN
공식 코드Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
https://github.com/carrierlxk/COSNet
공식 코드See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
https://github.com/carrierlxk/GraphMemVOS
공식 코드Video Object Segmentation with Episodic Graph Memory Networks
https://github.com/carrierlxk/MuG
공식 코드https://github.com/chikap421/mseg_vcuq
공식 코드https://github.com/chikap421/videosam
공식 코드VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation
https://github.com/choi58/mtcm
공식 코드Multi-Context Temporal Consistent Modeling for Referring Video Object Segmentation
https://github.com/cilinyan/VISA
공식 코드VISA: Reasoning Video Object Segmentation via Large Language Models
https://github.com/cilinyan/revos-api
공식 코드VISA: Reasoning Video Object Segmentation via Large Language Models
https://github.com/claudiacuttano/samwise
공식 코드SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
https://github.com/cmsflash/global-context-module
공식 코드Fast Video Object Segmentation using the Global Context Module
https://github.com/congvvc/instructseg
공식 코드InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
https://github.com/cvlab-kaist/SOLA
공식 코드Referring Video Object Segmentation via Language-aligned Track Selection
https://github.com/cxqnet/masseg
공식 코드https://github.com/danielshkao/thinkvideo
공식 코드ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of Thoughts
https://github.com/deeptag/cstm
공식 코드Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation
https://github.com/dexianghong/manet
공식 코드Multi-Attention Network for Compressed Video Referring Object Segmentation
https://github.com/dibschat/tempAgg
공식 코드Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dipika-singhania/C2F-TCN
공식 코드Coarse to Fine Multi-Resolution Temporal Convolutional Network
https://github.com/dlut-yyc/isomer
공식 코드Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation
https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
https://github.com/dukebw/SSTVOS
공식 코드SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
https://github.com/dut-csj/pvuw2023-vss-3rd
공식 코드3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW
https://github.com/dvl-tum/e-osvos
공식 코드https://github.com/dzh19990407/lbdt
공식 코드Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation
https://github.com/epic-kitchens/visor-hos
공식 코드EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.