https://github.com/happy-hsy/motionmae
공식 코드Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
Papers with Code 아카이브
Video Object Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 7
- 평가표
- 13
- 보존된 평가 기록
- 96
Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.
For leaderboards please refer to the different subtasks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/harkiratbehl/MetaVOS
공식 코드Meta Learning Deep Visual Words for Fast Video Object Segmentation
https://github.com/harrytea/detect-anyshadow
공식 코드Detect Any Shadow: Segment Anything for Video Shadow Detection
https://github.com/hebeifast/eventlowlightvos
공식 코드https://github.com/henghuiding/MOSE-api
공식 코드MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
https://github.com/henghuiding/MeViS
공식 코드MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
https://github.com/henghuiding/Vision-Language-Transformer
공식 코드VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
https://github.com/hengliusky/few_shot_rvos
공식 코드Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples
https://github.com/heshuting555/dshmp
공식 코드Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
https://github.com/hkchengrex/Cutie
공식 코드https://github.com/hkchengrex/MiVOS
공식 코드https://github.com/hkchengrex/STCN
공식 코드Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
https://github.com/hkchengrex/Tracking-Anything-with-DEVA
공식 코드https://github.com/hkchengrex/XMem
공식 코드XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/hongje/hmmn
공식 코드Hierarchical Memory Matching Network for Video Object Segmentation
https://github.com/hy0523/mtnet
공식 코드Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
https://github.com/hydragon516/dpa
공식 코드Dual Prototype Attention for Unsupervised Video Object Segmentation
https://github.com/hydragon516/gsanet
공식 코드Guided Slot Attention for Unsupervised Video Object Segmentation
https://github.com/hzxie/RMNet
공식 코드Efficient Regional Memory Network for Video Object Segmentation
https://github.com/imatge-upc/rvos
공식 코드RVOS: End-to-End Recurrent Network for Video Object Segmentation
https://github.com/imatge-upc/rvos-mots
공식 코드https://github.com/imatge-upc/synthref
공식 코드SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation
https://github.com/insomnia94/FTMU
공식 코드Fast Template Matching and Update for Video Object Tracking and Segmentation
https://github.com/isee-laboratory/referdino-plus
공식 코드ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025
https://github.com/jerryx1110/robust-video-object-segmentation
공식 코드Towards Robust Video Object Segmentation with Adaptive Object Calibration
https://github.com/jerryx1110/rpcmvos
공식 코드Reliable Propagation-Correction Modulation for Video Object Segmentation
https://github.com/jhl-det/rgbd_video_sod
공식 코드ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
https://github.com/jiawen-zhu/hqtrack
공식 코드https://github.com/jiawen-zhu/trackgpt
공식 코드https://github.com/jimmy-dq/dropmae
공식 코드DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
https://github.com/jovanavidenovic/dam4sam
공식 코드A Distractor-Aware Memory for Visual Object Tracking with SAM2
https://github.com/kai422/covos
공식 코드Accelerating Video Object Segmentation with Compressed Video
https://github.com/kangben258/MCITrack
공식 코드Exploring Enhanced Contextual Information for Video-Level Object Tracking
https://github.com/lancasterli/refsam
공식 코드RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
https://github.com/lanmng/sitvos
공식 코드Siamese Network with Interactive Transformer for Video Object Segmentation
https://github.com/leipingjie/sam2video
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.