Video Frame Interpolation On Ucf101 1

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PSNR

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
EMA-VFI	35.48	Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation
UPR-Net LARGE	35.47	A Unified Pyramid Recurrent Network for Video Frame Interpolation
VFIMamba	35.45	VFIMamba: Video Frame Interpolation with State Space Models
DQBC	35.44	Video Frame Interpolation with Densely Queried Bilateral Correlation
MA-CSPA	35.43	Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
IFRNet	35.42	IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
EBME-H*	35.41	Enhanced Bi-directional Motion Estimation for Video Frame Interpolation
SoftSplat	35.39	Softmax Splatting for Video Frame Interpolation
ABME	35.38	Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
CURE	35.36	Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
NCM-Base	35.36	Neighbor Correspondence Matching for Flow-based Video Frame Synthesis
FILM	35.32	FILM: Frame Interpolation for Large Motion
CDFI	35.21	CDFI: Compression-Driven Network Design for Frame Interpolation
M2M-PWC	35.17	Many-to-many Splatting for Efficient Video Frame Interpolation
BMBC	35.15	BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
DAIN	34.99	Depth-Aware Video Frame Interpolation
RRIN	34.93	Video Frame Interpolation via Residue Refinement
ST-MFNet	33.384	ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
CycleSuperSloMo	-	Unsupervised Video Interpolation Using Cycle Consistency

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Video Frame Interpolation On Ucf101 1 | SOTA | HyperAI