# Look here! A parametric learning based approach to redirect visual attention

arXiv: https://arxiv.org/abs/2008.05413 · HTML: https://mlsanity.com/paper/2008.05413/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Saliency Prior for Reducing Visual Distraction](https://mlsanity.com/paper/2109.01980/) (2021)
- [Realistic Saliency Guided Image Enhancement](https://mlsanity.com/paper/2306.06092/) (2023)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding](https://mlsanity.com/paper/2205.06934/) (2022)
- [GazeFusion: Saliency-Guided Image Generation](https://mlsanity.com/paper/2407.04191/) (2024)
- [Salient Mask-Guided Vision Transformer for Fine-Grained Classification](https://mlsanity.com/paper/2305.07102/) (2023)
- [Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control](https://mlsanity.com/paper/2307.14510/) (2023)
- [Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency](https://mlsanity.com/paper/2406.18588/) (2024)
- [Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media](https://mlsanity.com/paper/2501.12289/) (2025)
- [Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model](https://mlsanity.com/paper/2302.07440/) (2023)
- [Exploring Saliency Bias in Manipulation Detection](https://mlsanity.com/paper/2402.07338/) (2024)
- [Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video](https://mlsanity.com/paper/2609.21624/) (2026)

## Abstract (arXiv)

Across photography, marketing, and website design, being able to direct the viewer's attention is a powerful tool. Motivated by professional workflows, we introduce an automatic method to make an image region more attention-capturing via subtle image edits that maintain realism and fidelity to the original. From an input image and a user-provided mask, our GazeShiftNet model predicts a distinct set of global parametric transformations to be applied to the foreground and background image regions separately. We present the results of quantitative and qualitative experiments that demonstrate improvements over prior state-of-the-art. In contrast to existing attention shifting algorithms, our global parametric approach better preserves image semantics and avoids typical generative artifacts. Our edits enable inference at interactive rates on any image size, and easily generalize to videos. Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region. Furthermore, users can customize the edited images by dialing the edits up or down via interpolations in parameter space. This paper presents a practical tool that can simplify future image editing pipelines.