Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.
#计算机科学#[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for k...
#计算机科学#[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
#计算机科学#[ICCV 2023] Tracking Anything with Decoupled Video Segmentation
[CVPR'23] Universal Instance Perception as Object Discovery and Retrieval
[CVPR2024 Highlight]GLEE: General Object Foundation Model for Images and Videos at Scale
SAM-PT: Extending SAM to zero-shot video segmentation with point-based tracking.
[ECCV'22 Oral] Towards Grand Unification of Object Tracking
#计算机科学#[CVPR 2024 Highlight] Putting the Object Back Into Video Object Segmentation
#计算机科学#[NeurIPS 2021] Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
#计算机科学#[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion. Semi-supervised VOS as well!
[ICCV 2023] MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
[NeurIPS'21] Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking an...
🔖 Curated list of video object segmentation (VOS) papers, datasets, and projects.
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks (CVPR19)
The official implementation of CFBI(+): Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration.
#计算机科学#PyTorch re-implementation of DeepMask
Learning Unsupervised Video Object Segmentation through Visual Attention (CVPR19, PAMI20)
Zero-shot Video Object Segmentation via Attentive Graph Neural Networks (ICCV2019 Oral)