# Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination

arXiv: https://arxiv.org/abs/1812.01210 · HTML: https://mlsanity.com/paper/1812.01210/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation](https://mlsanity.com/paper/2205.14620/) (2022)
- [CDFI: Compression-Driven Network Design for Frame Interpolation](https://mlsanity.com/paper/2103.10559/) (2021)
- [RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.14186/) (2022)
- [Many-to-many Splatting for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2204.03513/) (2022)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [NTIRE 2021 Challenge on Video Super-Resolution](https://mlsanity.com/paper/2104.14852/) (2021)
- [FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing](https://mlsanity.com/paper/2203.16168/) (2022)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Long-term Video Frame Interpolation via Feature Propagation](https://mlsanity.com/paper/2203.15427/) (2022)
- [Sparsity-guided Network Design for Frame Interpolation](https://mlsanity.com/paper/2209.04551/) (2022)
- [H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions](https://mlsanity.com/paper/2211.11309/) (2022)
- [Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement](https://mlsanity.com/paper/2310.18946/) (2023)
- [Problem-dependent attention and effort in neural networks with applications to image resolution and model selection](https://mlsanity.com/paper/2201.01415/) (2022)
- [Error-Aware Spatial Ensembles for Video Frame Interpolation](https://mlsanity.com/paper/2207.12305/) (2022)
- [Multiple Hypothesis Flow Estimation for Video Frame Interpolation under Matching Ambiguity](https://mlsanity.com/paper/2608.07120/) (2026)

## Abstract (arXiv)

We propose a light-weight video frame interpolation algorithm. Our key innovation is an instance-level supervision that allows information to be learned from the high-resolution version of similar objects. Our experiment shows that the proposed method can generate state-of-the-art results across different datasets, with fractional computation resources (time and memory) of competing methods. Given two image frames, a cascade network creates an intermediate frame with 1) a flow-warping module that computes coarse bi-directional optical flow and creates an interpolated image via flow-based warping, followed by 2) an image synthesis module to make fine-scale corrections. In the learning stage, object detection proposals are generated on the interpolated image.Lower resolution objects are zoomed into, and the learning algorithms using an adversarial loss trained on high-resolution objects to guide the system towards the instance-level refinement corrects details of object shape and boundaries.