EyeFormer: A Transformer-Guided Reinforcement Learning Model for Predicting Personalized Scanpaths Across Different Visual Stimuli

Centrala begrepp

EyeFormer is a novel deep reinforcement learning model that leverages a Transformer architecture to predict personalized scanpaths, capturing both spatial and temporal characteristics of human gaze behavior across diverse visual stimuli including graphical user interfaces and natural scenes.

Sammanfattning

The paper introduces EyeFormer, a deep reinforcement learning model that uses a Transformer architecture to predict personalized scanpaths. The key highlights are:

EyeFormer is the first model capable of predicting personalized scanpaths for individual viewers by updating the viewer embedding with a few sample scanpaths from the target viewer. This allows the model to capture each viewer's unique viewing preferences and behaviors.

For population-level scanpath prediction, EyeFormer outperforms state-of-the-art models on a wide range of evaluation metrics, including dynamic time warping, time delay embedding, Eyenalysis, dynamic time warping with duration, and MultiMatch, on both graphical user interfaces (GUIs) and natural scenes.

EyeFormer accurately predicts both the spatial (fixation locations) and temporal (fixation order and duration) characteristics of scanpaths, which is crucial for applications like GUI layout optimization.

The model uses a Transformer architecture as the policy network to guide a deep reinforcement learning algorithm in generating the scanpaths. This allows it to effectively model the long-range sequential dependencies in the scanpath data.

The authors demonstrate an application of personalized GUI layout optimization using the personalized scanpath predictions from EyeFormer.

Statistik

"From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus."
"Large individual differences have been reported in viewing patterns [18]."
"Scanpaths are, therefore, first-order models of human vision from which second-order measurements such as saliency maps can be derived, but not the other way around."

Citat

"EyeFormer is the first model to predict full scanpaths at both individual and population levels, including fixations with coordinates and durations."
"EyeFormer accurately predicts both spatial (where) and temporal (order, duration) characteristics of scanpaths on both GUIs and natural scenes."

Viktiga insikter från

EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning

by Yue Jiang,Zi... på arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10163.pdf

Djupare frågor

How can the personalized scanpath predictions from EyeFormer be leveraged to improve the design and layout of other user interfaces beyond GUIs, such as virtual reality or augmented reality environments

The personalized scanpath predictions from EyeFormer can be instrumental in enhancing the design and layout of various user interfaces beyond GUIs, including virtual reality (VR) and augmented reality (AR) environments. In VR and AR, where the user's visual attention plays a crucial role in the overall experience, understanding individual viewing behaviors can significantly improve the user interaction and immersion. By predicting personalized scanpaths, EyeFormer can help optimize the placement of interactive elements, objects, and information within the VR or AR environment to align with the user's natural viewing tendencies. This can lead to a more intuitive and engaging user experience, ensuring that important elements are strategically positioned to capture the user's attention effectively. Additionally, personalized scanpath predictions can aid in creating adaptive interfaces that adjust in real-time based on the user's gaze behavior, providing a more personalized and immersive experience in VR and AR applications.

What other applications beyond GUI layout optimization could benefit from the ability to predict personalized scanpaths, and how might those applications be developed

The ability to predict personalized scanpaths using EyeFormer opens up a wide range of applications beyond GUI layout optimization. One such application could be in the field of marketing and advertising, where understanding individual viewing behaviors can help tailor advertisements and promotional content to capture and retain viewer attention effectively. By analyzing personalized scanpaths, marketers can optimize the placement of key information, products, or branding elements to maximize engagement and conversion rates. Another potential application could be in educational technology, where personalized scanpath predictions can be used to enhance e-learning platforms. By tracking and analyzing individual viewing behaviors, educational content can be dynamically adjusted to cater to the learner's preferences and attention patterns, leading to a more personalized and effective learning experience. Furthermore, personalized scanpath predictions could be valuable in the field of user experience (UX) design, where designers can use insights from individual viewing behaviors to create more user-centric and intuitive interfaces across various digital platforms and devices.

Given the model's strong performance on both GUIs and natural scenes, what insights could be gained by applying EyeFormer to analyze and compare viewing behaviors across these very different types of visual stimuli

Applying EyeFormer to analyze and compare viewing behaviors across GUIs and natural scenes can provide valuable insights into how individuals interact with different types of visual stimuli. By studying the similarities and differences in scanpaths between GUIs and natural scenes, researchers and designers can gain a deeper understanding of how environmental factors influence visual attention and perception. For example, comparing scanpaths in GUIs (which are structured and information-dense) to those in natural scenes (which are more organic and varied) can reveal how individuals adapt their viewing behaviors based on the context and content presented to them. These insights can inform the design of more effective and engaging visual content across different platforms and environments. Additionally, analyzing viewing behaviors across diverse stimuli can help identify universal patterns in human visual attention and cognition, leading to advancements in areas such as human-computer interaction, cognitive psychology, and visual communication.