Learning Gaussian Representation for Eye Fixation Prediction: A Novel Approach

Conceitos essenciais

Introducing a novel approach using Gaussian Mixture Models for eye fixation prediction, providing robustness and efficiency in real-time processing.

Resumo

Abstract: Introduces the concept of Gaussian Representation for eye fixation modeling.
Introduction: Discusses the importance of saliency detection and the difference between eye fixation prediction and salient object detection.
Bottom-Up vs. Top-Down Methods: Explains the attention mechanisms and methods used in computer vision.
Standard Pipeline: Details the process of generating dense fixation maps from raw fixation points.
Proposed Method: Introduces modeling dense fixation maps with Gaussian Mixture Models for improved generalization ability.
Experimental Results: Demonstrates the effectiveness and efficiency of the proposed method on three public datasets.
Related Work: Provides an overview of existing eye fixation prediction models and GMM-related models.

Estatísticas

"Our model with ResNet18 as the backbone (“SalGMM-ResNet18”) achieves comparable performance to the state-of-the-art model (MD-SEM [9]) while being seven times faster."
"For images from the SALICON dataset, we randomly select 70% of participants and then obtain the dense eye fixation map on the selected eye fixation points."

Citações

"Our contributions are summarized as follows: i. We formulate the eye fixation map as a Gaussian Mixture Model. ii. With annotations, we design a novel reconstruction loss for learning GMM parameters. iii. Abalation studies show real-time performing models."

Principais Insights Extraídos De

Learning Gaussian Representation for Eye Fixation Prediction

by Peipei Song,... às arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14821.pdf

Perguntas Mais Profundas

How does incorporating lightweight backbones like ShuffleNet impact model performance?

Incorporating lightweight backbones like ShuffleNet can have a significant impact on model performance. These lightweight architectures are designed to be computationally efficient and have fewer parameters compared to larger models like ResNet. This results in faster inference times and lower memory requirements, making them ideal for deployment on resource-constrained devices such as mobile phones or edge devices.
While the reduced complexity of lightweight backbones may lead to some loss in accuracy compared to larger models, they still offer competitive performance. In the context of eye fixation prediction, using ShuffleNet as a backbone can provide real-time processing capabilities without compromising too much on accuracy. The trade-off between speed and accuracy is often acceptable in applications where efficiency is crucial.

How does subjective annotation impact predicting probability distributions over pixel-wise maps?

Subjective annotation introduces variability into the ground truth data used for training predictive models. In the context of eye fixation prediction, where fixation points are recorded based on human participants' gaze behavior, this subjectivity can manifest as differences in individual attention patterns.
When predicting probability distributions over pixel-wise maps for eye fixation, this subjectivity can pose challenges. The generated dense fixation maps may not accurately represent true human fixation behavior due to variations among individuals. As a result, directly regressing towards these dense maps may lead to overfitting or unstable training.
By modeling eye fixations as Gaussian Mixture Models (GMMs) instead of relying solely on pixel-wise regression losses from annotated data, the impact of subjective annotations can be mitigated. GMMs provide a more robust representation that captures the uncertainty and variability inherent in human gaze behavior across different viewers.

How can this approach be extended to other fields beyond computer vision?

The approach of modeling eye fixations using Gaussian Mixture Models (GMMs) has implications beyond just computer vision applications:

Natural Language Processing: GMMs could potentially be applied in text analysis tasks such as sentiment analysis or topic modeling by representing textual data as mixtures of probability distributions.

Healthcare: GMMs could aid in medical image analysis for tasks like tumor detection or anomaly identification by capturing uncertainties within diagnostic images.

Finance: GMMs might find use cases in financial forecasting models by incorporating probabilistic representations for market trends or risk assessment.

Recommendation Systems: GMMs could enhance recommendation algorithms by considering user preferences as mixture components with varying probabilities.

By adapting the concept of GMM-based representation and learning from probabilistic distributions across different domains, it's possible to improve model robustness and account for uncertainties inherent in various datasets beyond traditional computer vision tasks.