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 581–590 of 895 papers

Title	Date	Tasks	Status	Hype
Contrastive Transformation for Self-supervised Correspondence Learning	Dec 9, 2020	Object TrackingSelf-Supervised Learning	CodeCode Available	1
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation	Dec 4, 2020	Semantic SegmentationUnsupervised Video Object Segmentation	—Unverified	0
Make One-Shot Video Object Segmentation Efficient Again	Dec 3, 2020	Objectobject-detection	CodeCode Available	1
UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information	Nov 29, 2020	Aerial Video Semantic SegmentationDecision Making	CodeCode Available	0
High Fidelity Interactive Video Segmentation Using Tensor Decomposition Boundary Loss Convolutional Tessellations and Context Aware Skip Connections	Nov 23, 2020	Interactive SegmentationSegmentation	—Unverified	0
TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss	Nov 9, 2020	ObjectObject Localization	CodeCode Available	1
Learning Representations from Audio-Visual Spatial Alignment	Nov 3, 2020	Action RecognitionRepresentation Learning	—Unverified	0
Highway Driving Dataset for Semantic Video Segmentation	Nov 2, 2020	Autonomous DrivingImage Segmentation	—Unverified	0
Reducing the Annotation Effort for Video Object Segmentation Datasets	Nov 2, 2020	ObjectSemantic Segmentation	—Unverified	0
Actor and Action Modular Network for Text-based Video Segmentation	Nov 2, 2020	Action SegmentationAction Understanding	—Unverified	0

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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