Video Semantic Segmentation

The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 491–500 of 895 papers

Title	Date	Tasks	Status
Addressing Issues with Working Memory in Video Object Segmentation	Oct 29, 2024	Inductive BiasObject	—Unverified
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking	Dec 22, 2021	ObjectObject Tracking	—Unverified
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery	Sep 25, 2023	Autonomous DrivingObject	—Unverified
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos	Oct 1, 2019	Action RecognitionAutonomous Driving	—Unverified
A Fast Two Pass Multi-Value Segmentation Algorithm based on Connected Component Analysis	Feb 11, 2014	SegmentationVideo Segmentation	—Unverified
Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation	Jan 1, 2023	RetrievalSemantic Segmentation	—Unverified
A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages	Jun 1, 2022	Machine TranslationVideo Segmentation	—Unverified
An Efficient 3D CNN for Action/Object Segmentation in Video	Jul 21, 2019	Action SegmentationDecoder	—Unverified
An Empirical Study of Propagation-based Methods for Video Object Segmentation	Jul 30, 2019	ObjectSemantic Segmentation	—Unverified
An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos	Nov 30, 2017	Action DetectionAction Segmentation	—Unverified

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All datasets Cityscapes val CamVid VSPW LaRS Multispectral Video Semantic Segmentation

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TMANet-50	mIoU	80.3	—	Unverified
2	TDNet-50 [9]	mIoU	79.9	—	Unverified
3	DeltaDist-DDRNet-39	mIoU	79.9	—	Unverified
4	PSPNet-101 [20]	mIoU	79.7	—	Unverified
5	PSPNet-50 [20]	mIoU	78.1	—	Unverified
6	LVS [12]	mIoU	76.8	—	Unverified
7	GRFP [15]	mIoU	73.6	—	Unverified
8	FCN-50 [14]	mIoU	70.1	—	Unverified
9	DFF [22]	mIoU	69.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TMANet-50	Mean IoU	76.5	—	Unverified
2	ETC-MobileNet	Mean IoU	76.3	—	Unverified
3	TDNet-50	Mean IoU	76.2	—	Unverified
4	PSPNet-50	Mean IoU	76	—	Unverified
5	Netwarp	Mean IoU	74.7	—	Unverified
6	GRFP	Mean IoU	67.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DVIS++(VIT-L)	mIoU	63.8	—	Unverified
2	UniVS(Swin-L)	mIoU	59.8	—	Unverified
3	Tube-Link(Swin-large)	mIoU	59.6	—	Unverified
4	MRCFA(MiT-B5)	mIoU	49.9	—	Unverified
5	CFFM(MiT-B5)	mIoU	49.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WaSR-T (ResNet-101)	Q	60.1	—	Unverified
2	TMANet (ResNet-50)	Q	57.5	—	Unverified
3	CSANet (ResNet-101)	Q	49.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MVNet(DeepLabV3)	mIoU	54.52	—	Unverified
2	MVNet(PSPNet)	mIoU	54.36	—	Unverified
3	MVNet(FCN)	mIoU	53.9	—	Unverified