# Patch-level Gaze Distribution Prediction for Gaze Following

arXiv: https://arxiv.org/abs/2211.11062 · HTML: https://mlsanity.com/paper/2211.11062/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Detecting Attended Visual Targets in Video](https://mlsanity.com/paper/2003.02501/) (2020)
- [Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency](https://mlsanity.com/paper/1807.10437/) (2018)
- [End-to-End Human-Gaze-Target Detection with Transformers](https://mlsanity.com/paper/2203.10433/) (2022)
- [Believe It or Not, We Know What You Are Looking at!](https://mlsanity.com/paper/1907.02364/) (2019)
- [GOO: A Dataset for Gaze Object Prediction in Retail Environments](https://mlsanity.com/paper/2105.10793/) (2021)
- [GaTector: A Unified Framework for Gaze Object Prediction](https://mlsanity.com/paper/2112.03549/) (2021)
- [Gaze Estimation for Assisted Living Environments](https://mlsanity.com/paper/1909.09225/) (2019)
- [Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View](https://mlsanity.com/paper/1902.10953/) (2019)

## Built on by (most cited first, 22 of 22)

- [TrojVLM: Backdoor Attack Against Vision Language Models](https://mlsanity.com/paper/2409.19232/) (2024)
- [Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders](https://mlsanity.com/paper/2412.09586/) (2024)
- [Object-aware Gaze Target Detection](https://mlsanity.com/paper/2307.09662/) (2023)
- [ViTGaze: Gaze Following with Interaction Features in Vision Transformers](https://mlsanity.com/paper/2403.12778/) (2024)
- [GazeVLM: A Vision-Language Model for Multi-Task Gaze Understanding](https://mlsanity.com/paper/2511.06348/) (2025)
- [Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following](https://mlsanity.com/paper/2406.02774/) (2024)
- [Multi-view Gaze Target Estimation](https://mlsanity.com/paper/2508.05857/) (2025)
- [A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction](https://mlsanity.com/paper/2403.10511/) (2024)
- [Driver Attention Tracking and Analysis](https://mlsanity.com/paper/2404.07122/) (2024)
- [Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection](https://mlsanity.com/paper/2409.17886/) (2024)
- [AL-GTD: Deep Active Learning for Gaze Target Detection](https://mlsanity.com/paper/2409.18561/) (2024)
- [GaTector+: A Unified Head-free Framework for Gaze Object and Gaze Following Prediction](https://mlsanity.com/paper/2510.25301/) (2025)
- [Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models](https://mlsanity.com/paper/2605.19859/) (2026)
- [Enhancing Gaze Reasoning in Vision Foundation Models for Gaze Following](https://mlsanity.com/paper/2605.22607/) (2026)
- [OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following](https://mlsanity.com/paper/2605.26399/) (2026)
- [Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning](https://mlsanity.com/paper/2606.29334/) (2026)
- [PAGE: Towards Practical Human-level Gaze Target Estimation](https://mlsanity.com/paper/2607.04860/) (2026)
- [TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues](https://mlsanity.com/paper/2607.10130/) (2026)
- [Open-Vocabulary Gaze Object Prediction: Benchmark and Method](https://mlsanity.com/paper/2607.18827/) (2026)
- [Gaze Target Estimation Anywhere with Concepts](https://mlsanity.com/paper/2608.11367/) (2026)
- [From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation](https://mlsanity.com/paper/2609.06208/) (2026)
- [Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching](https://mlsanity.com/paper/2609.07415/) (2026)

## Abstract (arXiv)

Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located outside the image. Recent works detect the gaze target by training a heatmap regression task with a pixel-wise mean-square error (MSE) loss, while formulating the in/out prediction task as a binary classification task. This training formulation puts a strict, pixel-level constraint in higher resolution on the single annotation available in training, and does not consider annotation variance and the correlation between the two subtasks. To address these issues, we introduce the patch distribution prediction (PDP) method. We replace the in/out prediction branch in previous models with the PDP branch, by predicting a patch-level gaze distribution that also considers the outside cases. Experiments show that our model regularizes the MSE loss by predicting better heatmap distributions on images with larger annotation variances, meanwhile bridging the gap between the target prediction and in/out prediction subtasks, showing a significant improvement in performance on both subtasks on public gaze following datasets.