# ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation

arXiv: https://arxiv.org/abs/2009.01005 · HTML: https://mlsanity.com/paper/2009.01005/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

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

- [Spatio-Temporal Representation Factorization for Video-based Person Re-Identification](https://mlsanity.com/paper/2107.11878/) (2021)
- [Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations](https://mlsanity.com/paper/2110.01823/) (2021)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [Blur Interpolation Transformer for Real-World Motion from Blur](https://mlsanity.com/paper/2211.11423/) (2022)
- [Cross-Domain Video Anomaly Detection without Target Domain Adaptation](https://mlsanity.com/paper/2212.07010/) (2022)
- [DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting](https://mlsanity.com/paper/2111.09985/) (2021)
- [GAMA: Generative Adversarial Multi-Object Scene Attacks](https://mlsanity.com/paper/2209.09502/) (2022)
- [Learning Cross-Video Neural Representations for High-Quality Frame Interpolation](https://mlsanity.com/paper/2203.00137/) (2022)
- [Towards Interpretable Video Super-Resolution via Alternating Optimization](https://mlsanity.com/paper/2207.10765/) (2022)
- [Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks](https://mlsanity.com/paper/2209.09883/) (2022)
- [Deep Quantized Representation for Enhanced Reconstruction](https://mlsanity.com/paper/2107.14368/) (2021)
- [Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning](https://mlsanity.com/paper/2108.02832/) (2021)
- [Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration](https://mlsanity.com/paper/2307.14866/) (2023)
- [Video frame interpolation for high dynamic range sequences captured with dual-exposure sensors](https://mlsanity.com/paper/2206.09485/) (2022)
- [Three-Stage Cascade Framework for Blurry Video Frame Interpolation](https://mlsanity.com/paper/2310.05383/) (2023)

## Abstract (arXiv)

Existing works address the problem of generating high frame-rate sharp videos by separately learning the frame deblurring and frame interpolation modules. Most of these approaches have a strong prior assumption that all the input frames are blurry whereas in a real-world setting, the quality of frames varies. Moreover, such approaches are trained to perform either of the two tasks - deblurring or interpolation - in isolation, while many practical situations call for both. Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry. We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos with no prior knowledge of input frames being blurry or not, thereby performing the task of both deblurring and interpolation. We hypothesize that information from the latent representation of the consecutive frames can be utilized to generate optimized representations for both frame deblurring and frame interpolation. Specifically, we employ combination of self-attention and cross-attention module between consecutive frames in the latent space to generate optimized representation for each frame. The optimized representation learnt using these attention modules help the model to generate and interpolate sharp frames. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches, even though we tackle a much more difficult problem.