Video Object Segmentation
243 papers with code • 9 benchmarks • 17 datasets
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.
Benchmarks
These leaderboards are used to track progress in Video Object Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Video Object Segmentation models and implementationsDatasets
DAVIS
DAVIS 2017
DAVIS 2016
YouTube-VOS 2018
FBMS
SegTrack-v2
Referring Expressions for DAVIS 2016 & 2017
MOSE
BL30K
LVOS
Subtasks
Latest papers
Flexible visual prompts for in-context learning in computer vision
Additionally, we propose a technique for support set selection, which involves choosing the most relevant images to include in this set.
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target.
Putting the Object Back into Video Object Segmentation
hkchengrex/Cutie
•
•
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.
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
suhwan-cho/tmo
•
•
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking.
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
Tracking Anything with Decoupled Video Segmentation
hkchengrex/Tracking-Anything-with-DEVA
•
•
ICCV 2023
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.
Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples
hengliusky/few_shot_rvos
•
•
ICCV 2023
Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene.
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
Tracking any given object(s) spatially and temporally is a common purpose in Visual Object Tracking (VOT) and Video Object Segmentation (VOS).
