We describe a neural attention model with a learnable retinal sampling
lattice. The model is trained on a visual search task requiring the
classification of an object embedded in a visual scene amidst background
distractors using the smallest number of fixations. We explore the tiling
properties that emerge in the model's retinal sampling lattice after training.
Specifically, we show that this lattice resembles the eccentricity dependent
sampling lattice of the primate retina, with a high resolution region in the
fovea surrounded by a low resolution periphery. Furthermore, we find conditions
where these emergent properties are amplified or eliminated providing clues to
their function.

描述了一种具有可学习的视网膜采样晶格的神经注意模型，在视觉搜索任务中进行训练，在需要使用最少的凝视来分类视觉场景中嵌入的物体的背景干扰时。我们研究了模型在训练后出现的平铺性质。在模型的视网膜采样晶格中，高分辨率区域位于中央凹周围的低分辨率区域，类似于灵长类动物视网膜的离心依赖采样晶格。此外，我们发现了放大或消除这些新发性属性的条件，提供了它们的功能线索。

在视觉场景中学会注意力，出现了中央凹采样

Emergence of foveal image sampling from learning to attend in visual  scenes

Salient object detection or salient region detection models, diverging from
fixation prediction models, have traditionally been dealing with locating and
segmenting the most salient object or region in a scene. While the notion of
most salient object is sensible when multiple objects exist in a scene, current
datasets for evaluation of saliency detection approaches often have scenes with
only one single object. We introduce three main contributions in this paper:
First, we take an indepth look at the problem of salient object detection by
studying the relationship between where people look in scenes and what they
choose as the most salient object when they are explicitly asked. Based on the
agreement between fixations and saliency judgments, we then suggest that the
most salient object is the one that attracts the highest fraction of fixations.
Second, we provide two new less biased benchmark datasets containing scenes
with multiple objects that challenge existing saliency models. Indeed, we
observed a severe drop in performance of 8 state-of-the-art models on our
datasets (40% to 70%). Third, we propose a very simple yet powerful model based
on superpixels to be used as a baseline for model evaluation and comparison.
While on par with the best models on MSRA-5K dataset, our model wins over other
models on our data highlighting a serious drawback of existing models, which is
convoluting the processes of locating the most salient object and its
segmentation. We also provide a review and statistical analysis of some labeled
scene datasets that can be used for evaluating salient object detection models.
We believe that our work can greatly help remedy the over-fitting of models to
existing biased datasets and opens new venues for future research in this
fast-evolving field.

通过研究人们在场景中关注的位置以及他们明确要求时选择的最显著对象之间的关系，我们建议吸引最多注视的对象是最显著的对象。我们提供了两个新的数据集，挑战现有的显着性模型，并提出了一种基于超像素的简单但强大的模型用于模型评估和比较。我们还提供了对一些标记场景数据集的评论和统计分析，这些数据集可用于评估显着对象检测模型。