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Channel Attention Is All You Need for Video Frame Interpolation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add channel attention to residual blocks (ablation) | CAIN · Vimeo90K | PSNR: 34.25 -> 34.65 | Helped |
| add channel attention to residual blocks (ablation) | CAIN · SNU-FILM Extreme | PSNR: 24.48 -> 24.69 | Helped |
| add channel attention to residual blocks (ablation) | CAIN · Middlebury | interpolation error: 2.36 -> 2.28 | Helped |
| increase ResGroups from 5 to 12 (fixed total blocks) (ablation) | CAIN · Vimeo90K | PSNR: 34.65 -> 34.03 | Hurt |
| flow-free channel-attention model vs flow-based DAIN | frame interpolation · Vimeo90K | PSNR: 34.72 -> 34.65 | Hurt |
| flow-free channel-attention model vs flow-based DAIN | frame interpolation · HD 1280x720 | run-time (ms): 816 -> 64 | Helped |
| CAIN vs SepConv-L1 | frame interpolation · UCF101 | PSNR: 34.95 -> 34.91 | No clear effect |
Closest papers by meaning
- Depth-Aware Video Frame Interpolation (CVPR 2019 · similarity 0.93)
- Video Frame Interpolation via Adaptive Convolution (CVPR 2017 · similarity 0.92)
- Context-Aware Synthesis for Video Frame Interpolation (CVPR 2018 · similarity 0.91)
- Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation (CVPR 2018 · similarity 0.91)
- Disentangled Motion Modeling for Video Frame Interpolation (AAAI 2025 · similarity 0.87)
- Sparse Global Matching for Video Frame Interpolation with Large Motion (CVPR 2024 · similarity 0.87)
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