Video Semantic Segmentation
320 papers with code • 5 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Video Semantic Segmentation
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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
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation
Then we render the output of optical flow net to a fully convolutional SegNet model.
Point-VOS: Pointing Up Video Object Segmentation
We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort.
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment.
Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes
The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes.
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
This is enabled by deformable attention mechanism, where the keys and values capturing the memory of a video sequence in the attention module have flexible locations updated across frames.
Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation
Interactive Video Object Segmentation (iVOS) is a challenging task that requires real-time human-computer interaction.
Understanding Video Transformers via Universal Concept Discovery
Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered.
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision
To address these issues, we propose 1) a more challenging reformulation of temporal self-supervision as frame-level (rather than clip-level) recognition tasks and 2) an effective augmentation strategy to mitigate shortcuts.
Appearance-based Refinement for Object-Centric Motion Segmentation
The goal of this paper is to discover, segment, and track independently moving objects in complex visual scenes.
Artificial intelligence optical hardware empowers high-resolution hyperspectral video understanding at 1.2 Tb/s
The technology platform combines artificial intelligence hardware, processing information optically, with state-of-the-art machine vision networks, resulting in a data processing speed of 1. 2 Tb/s with hundreds of frequency bands and megapixel spatial resolution at video rates.
