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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Video Frame Interpolation On Ucf101 1

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
PSNR
Paper TitleRepository
ST-MFNet33.384ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
ABME35.38Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
EBME-H*35.41Enhanced Bi-directional Motion Estimation for Video Frame Interpolation
SoftSplat35.39Softmax Splatting for Video Frame Interpolation
DQBC35.44Video Frame Interpolation with Densely Queried Bilateral Correlation
EMA-VFI35.48Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation
IFRNet35.42IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
MA-CSPA35.43Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation-
CDFI35.21CDFI: Compression-Driven Network Design for Frame Interpolation
CycleSuperSloMo-Unsupervised Video Interpolation Using Cycle Consistency
CURE35.36Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
BMBC35.15BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
RRIN34.93Video Frame Interpolation via Residue Refinement
DAIN34.99Depth-Aware Video Frame Interpolation
NCM-Base35.36Neighbor Correspondence Matching for Flow-based Video Frame Synthesis-
VFIMamba35.45VFIMamba: Video Frame Interpolation with State Space Models
FILM35.32FILM: Frame Interpolation for Large Motion
UPR-Net LARGE35.47A Unified Pyramid Recurrent Network for Video Frame Interpolation
M2M-PWC35.17Many-to-many Splatting for Efficient Video Frame Interpolation
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