Video Semantic Segmentation
321 papers with code • 5 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Video Semantic Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Video Semantic Segmentation models and implementations
Cityscapes
CamVid
SegTrack-v2
VSPW
MOSE
ODMS
LaRS
Latest papers with no code
Spatial-Temporal Multi-level Association for Video Object Segmentation
In addition, we propose a spatial-temporal memory to assist feature association and temporal ID assignment and correlation.
Event-assisted Low-Light Video Object Segmentation
In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation.
Annolid: Annotate, Segment, and Track Anything You Need
Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis.
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
In this work, we study the problem of common and unique feature extraction from noisy data.
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework
Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation.
Real-time Surgical Instrument Segmentation in Video Using Point Tracking and Segment Anything
Inspired by this progress, we present a novel framework that combines an online point tracker with a lightweight SAM model that is fine-tuned for surgical instrument segmentation.
ClickVOS: Click Video Object Segmentation
To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame.
Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
In this work, we introduce a test-time training (TTT) strategy to address the problem.
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation
Using optical flow to estimate the movement between consecutive frames, we can shift the prior term in the moving-average calculation to align with the geometry of the current frame.
Deep Common Feature Mining for Efficient Video Semantic Segmentation
Recent advancements in video semantic segmentation have made substantial progress by exploiting temporal correlations.
