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 641–650 of 895 papers

Title	Date	Tasks	Status	Hype
Real-Time Segmentation Networks should be Latency Aware	Apr 6, 2020	Autonomous VehiclesScene Segmentation	—Unverified	0
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries	Apr 3, 2020	Referring Expression SegmentationVideo Segmentation	—Unverified	0
Temporally Distributed Networks for Fast Video Semantic Segmentation	Apr 3, 2020	Knowledge DistillationReal-Time Semantic Segmentation	CodeCode Available	1
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation	Mar 30, 2020	Interactive Video Object SegmentationObject	—Unverified	0
TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge	Mar 30, 2020	GPUImage Segmentation	CodeCode Available	1
Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection	Mar 29, 2020	Action SegmentationSegmentation	—Unverified	0
Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net	Mar 26, 2020	Coronary Artery SegmentationSegmentation	—Unverified	0
Learning What to Learn for Video Object Segmentation	Mar 25, 2020	Few-Shot LearningObject	CodeCode Available	1
Collaborative Video Object Segmentation by Foreground-Background Integration	Mar 18, 2020	ObjectOne-shot visual object segmentation	CodeCode Available	1
Dual Temporal Memory Network for Efficient Video Object Segmentation	Mar 13, 2020	ObjectOne-shot visual object segmentation	—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