Predicting salient regions in natural images requires the detection of
objects that are present in a scene. To develop robust representations for this
challenging task, high-level visual features at multiple spatial scales must be
extracted and augmented with contextual information. However, existing models
aimed at explaining human fixation maps do not incorporate such a mechanism
explicitly. Here we propose an approach based on a convolutional neural network
pre-trained on a large-scale image classification task. The architecture forms
an encoder-decoder structure and includes a module with multiple convolutional
layers at different dilation rates to capture multi-scale features in parallel.
Moreover, we combine the resulting representations with global scene
information for accurately predicting visual saliency. Our model achieves
competitive and consistent results across multiple evaluation metrics on two
public saliency benchmarks and we demonstrate the effectiveness of the
suggested approach on five datasets and selected examples. Compared to state of
the art approaches, the network is based on a lightweight image classification
backbone and hence presents a suitable choice for applications with limited
computational resources, such as (virtual) robotic systems, to estimate human
fixations across complex natural scenes.

该论文提出了一种基于卷积神经网络的方法，通过提取多尺度的高级视觉特征和上下文信息，结合全局场景信息，用于准确预测视觉显著性，可以适用于资源受限的应用，如（虚拟）机器人系统，从而在多个基准测试数据集上取得了有竞争力和一致的结果。

基于上下文编码器 - 解码器网络的视觉显著性预测

Contextual Encoder-Decoder Network for Visual Saliency Prediction

Predicting human fixations from images has recently seen large improvements
by leveraging deep representations which were pretrained for object
recognition. However, as we show in this paper, these networks are highly
overparameterized for the task of fixation prediction. We first present a
simple yet principled greedy pruning method which we call Fisher pruning.
Through a combination of knowledge distillation and Fisher pruning, we obtain
much more runtime-efficient architectures for saliency prediction, achieving a
10x speedup for the same AUC performance as a state of the art network on the
CAT2000 dataset. Speeding up single-image gaze prediction is important for many
real-world applications, but it is also a crucial step in the development of
video saliency models, where the amount of data to be processed is
substantially larger.

通过 Fisher pruning 方法和知识蒸馏的结合，我们获得了更高效的视觉显著性预测体系结构，为单幅图像凝视预测的加速在现实世界应用和视频显著性模型开发中具有重要意义。

利用密集网络和 Fisher 剪枝加速凝视预测

Faster gaze prediction with dense networks and Fisher pruning

Previous work on predicting the target of visual search from human fixations
only considered closed-world settings in which training labels are available
and predictions are performed for a known set of potential targets. In this
work we go beyond the state of the art by studying search target prediction in
an open-world setting in which we no longer assume that we have fixation data
to train for the search targets. We present a dataset containing fixation data
of 18 users searching for natural images from three image categories within
synthesised image collages of about 80 images. In a closed-world baseline
experiment we show that we can predict the correct target image out of a
candidate set of five images. We then present a new problem formulation for
search target prediction in the open-world setting that is based on learning
compatibilities between fixations and potential targets.

本文研究了在不考虑已有固定数据的情况下，学习人类注视点以预测搜索目标的问题，并提出了一个新的方法来学习注视点与潜在目标之间的兼容性，实验证明可以对候选图像集中的正确目标进行预测。