# Attention-Aware Face Hallucination via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1708.03132 · HTML: https://mlsanity.com/paper/1708.03132/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 30 of 33)

- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs](https://mlsanity.com/paper/2201.06374/) (2022)
- [DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration](https://mlsanity.com/paper/2303.06885/) (2023)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Learning Dual Memory Dictionaries for Blind Face Restoration](https://mlsanity.com/paper/2210.08160/) (2022)
- [GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors](https://mlsanity.com/paper/2203.07319/) (2022)
- [RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs](https://mlsanity.com/paper/2308.07228/) (2023)
- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks](https://mlsanity.com/paper/2207.01580/) (2022)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Hallucination Improves the Performance of Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2307.12168/) (2023)
- [Multi-modality Deep Restoration of Extremely Compressed Face Videos](https://mlsanity.com/paper/2107.05548/) (2021)
- [EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset](https://mlsanity.com/paper/2110.05031/) (2021)
- [Blind Face Restoration: Benchmark Datasets and a Baseline Model](https://mlsanity.com/paper/2206.03697/) (2022)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning](https://mlsanity.com/paper/2102.06955/) (2021)
- [Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails](https://mlsanity.com/paper/2108.00602/) (2021)
- [Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration](https://mlsanity.com/paper/2206.13962/) (2022)
- [Super-Resolving Face Image by Facial Parsing Information](https://mlsanity.com/paper/2304.02923/) (2023)
- [HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars](https://mlsanity.com/paper/2203.14863/) (2022)
- [Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors](https://mlsanity.com/paper/2210.06002/) (2022)
- [HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods](https://mlsanity.com/paper/2309.07495/) (2023)
- [W-Net: A Facial Feature-Guided Face Super-Resolution Network](https://mlsanity.com/paper/2406.00676/) (2024)
- [From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution](https://mlsanity.com/paper/2108.13584/) (2021)
- [CCFace: Classification Consistency for Low-Resolution Face Recognition](https://mlsanity.com/paper/2308.09230/) (2023)
- [A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading](https://mlsanity.com/paper/2310.09462/) (2023)
- [Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection](https://mlsanity.com/paper/2601.12919/) (2026)
- [VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots](https://mlsanity.com/paper/2105.14954/) (2021)

## Abstract (arXiv)

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations.