Search Results for author:
Found 6 papers, 6 papers with code
Putting the Object Back into Video Object Segmentation
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on MOSE
Tracking Anything with Decoupled Video Segmentation
To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation.
Ranked #1 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
(using extra training data)
Open-Vocabulary Video Segmentation
Open-World Video Segmentation
+7
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model.
Ranked #1 on
Video Object Segmentation
on YouTube-VOS 2019
(using extra training data)
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation.
Ranked #7 on
Video Object Segmentation
on YouTube-VOS 2019
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.
Ranked #1 on
Interactive Video Object Segmentation
on DAVIS 2017
(using extra training data)
Interactive Video Object Segmentation
Semantic Segmentation
+2
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Ranked #1 on
Semantic Segmentation
on BIG
(using extra training data)
