Existing eye fixation prediction methods perform the mapping from input
images to the corresponding dense fixation maps generated from raw fixation
points. However, due to the stochastic nature of human fixation, the generated
dense fixation maps may be a less-than-ideal representation of human fixation.
To provide a robust fixation model, we introduce Gaussian Representation for
eye fixation modeling. Specifically, we propose to model the eye fixation map
as a mixture of probability distributions, namely a Gaussian Mixture Model. In
this new representation, we use several Gaussian distribution components as an
alternative to the provided fixation map, which makes the model more robust to
the randomness of fixation. Meanwhile, we design our framework upon some
lightweight backbones to achieve real-time fixation prediction. Experimental
results on three public fixation prediction datasets (SALICON, MIT1003,
TORONTO) demonstrate that our method is fast and effective.

通过引入高斯表示来建模眼球注视，我们设计了包含多个高斯分布组件的眼球注视地图混合模型，使得模型对注视的随机性更加稳健，并基于轻量级框架实现了实时注视预测。

学习高斯表示的眼球注视预测

Learning Gaussian Representation for Eye Fixation Prediction

We present a novel approach for saliency prediction in images, leveraging
parallel decoding in transformers to learn saliency solely from fixation maps.
Models typically rely on continuous saliency maps, to overcome the difficulty
of optimizing for the discrete fixation map. We attempt to replicate the
experimental setup that generates saliency datasets. Our approach treats
saliency prediction as a direct set prediction problem, via a global loss that
enforces unique fixations prediction through bipartite matching and a
transformer encoder-decoder architecture. By utilizing a fixed set of learned
fixation queries, the cross-attention reasons over the image features to
directly output the fixation points, distinguishing it from other modern
saliency predictors. Our approach, named Saliency TRansformer (SalTR), achieves
metric scores on par with state-of-the-art approaches on the Salicon and MIT300
benchmarks.

基于变压器模型的 Saliency TRansformer 方法通过利用变压器中的并行解码，仅从注视地图中学习显著性，并将显著性预测视为一种直接的集合预测问题，通过一种全局损失来预测唯一的注视点，并在 Salicon 和 MIT300 基准测试中取得与最先进方法相当的度量分数。