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 661–670 of 895 papers

Title	Date	Tasks	Status
ALBA : Reinforcement Learning for Video Object Segmentation	May 26, 2020	ObjectOne-shot visual object segmentation	CodeCode Available
Tamed Warping Network for High-Resolution Semantic Video Segmentation	May 4, 2020	Motion EstimationReal-Time Semantic Segmentation	—Unverified
MEDIAPI-SKEL - A 2D-Skeleton Video Database of French Sign Language With Aligned French Subtitles	May 1, 2020	Cross-Modal RetrievalRetrieval	—Unverified
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory	Apr 25, 2020	DecoderObject	CodeCode Available
LSM: Learning Subspace Minimization for Low-level Vision	Apr 20, 2020	Image SegmentationOptical Flow Estimation	—Unverified
Real-Time Segmentation Networks should be Latency Aware	Apr 6, 2020	Autonomous VehiclesScene Segmentation	—Unverified
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries	Apr 3, 2020	Referring Expression SegmentationVideo Segmentation	—Unverified
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation	Mar 30, 2020	Interactive Video Object SegmentationObject	—Unverified
Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection	Mar 29, 2020	Action SegmentationSegmentation	—Unverified
Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net	Mar 26, 2020	Coronary Artery SegmentationSegmentation	—Unverified

Show:10 25 50

← PrevPage 67 of 90Next →

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	DeltaDist-DDRNet-39	mIoU	79.9	—	Unverified
3	TDNet-50 [9]	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