# Unified Attention Modeling for Efficient Free-Viewing and Visual Search via Shared Representations

arXiv: https://arxiv.org/abs/2506.02764 · HTML: https://mlsanity.com/paper/2506.02764/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention](https://mlsanity.com/paper/2303.15274/) (2023)
- [State-of-the-Art in Human Scanpath Prediction](https://mlsanity.com/paper/2102.12239/) (2021)
- [Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers](https://mlsanity.com/paper/2303.09383/) (2023)
- [Target-absent Human Attention](https://mlsanity.com/paper/2207.01166/) (2022)
- [Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning](https://mlsanity.com/paper/2001.11921/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2606.20491/) (2026)

## Abstract (arXiv)

Computational human attention modeling in free-viewing and task-specific settings is often studied separately, with limited exploration of whether a common representation exists between them. This work investigates this question and proposes a neural network architecture that builds upon the Human Attention transformer (HAT) to test the hypothesis. Our results demonstrate that free-viewing and visual search can efficiently share a common representation, allowing a model trained in free-viewing attention to transfer its knowledge to task-driven visual search with a performance drop of only 3.86% in the predicted fixation scanpaths, measured by the semantic sequence score (SemSS) metric which reflects the similarity between predicted and human scanpaths. This transfer reduces computational costs by 92.29% in terms of GFLOPs and 31.23% in terms of trainable parameters.