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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Video Frame Interpolation On Vimeo90K

평가 지표

PSNR

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
PSNR
Paper TitleRepository
SepConv-L133.80Video Frame Interpolation via Adaptive Separable Convolution
ToFlow33.73Video Enhancement with Task-Oriented Flow
M2M-PWC35.4Many-to-many Splatting for Efficient Video Frame Interpolation
FILM36.06FILM: Frame Interpolation for Large Motion
BMBC35.01BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
ABME36.18Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
MEMC-Net*34.40MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
UPR-Net LARGE36.42A Unified Pyramid Recurrent Network for Video Frame Interpolation
NCM-Base35.88Neighbor Correspondence Matching for Flow-based Video Frame Synthesis-
IFRNet36.20IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
SoftSplat36.10Softmax Splatting for Video Frame Interpolation
CDFI35.17CDFI: Compression-Driven Network Design for Frame Interpolation
FRUCnet34.65--
DAIN34.71Depth-Aware Video Frame Interpolation
ECMNet34.95Enhanced Correlation Matching based Video Frame Interpolation-
EBME-H*36.19Enhanced Bi-directional Motion Estimation for Video Frame Interpolation
MA-CSPA36.76Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation-
RRIN35.22Video Frame Interpolation via Residue Refinement
XVFI35.07XVFI: eXtreme Video Frame Interpolation
DAIN34.71Depth-Aware Video Frame Interpolation
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