We propose a novel method for unsupervised semantic image segmentation based
on mutual information maximization between local and global high-level image
features. The core idea of our work is to leverage recent progress in
self-supervised image representation learning. Representation learning methods
compute a single high-level feature capturing an entire image. In contrast, we
compute multiple high-level features, each capturing image segments of one
particular semantic class. To this end, we propose a novel two-step learning
procedure comprising a segmentation and a mutual information maximization step.
In the first step, we segment images based on local and global features. In the
second step, we maximize the mutual information between local features and
high-level features of their respective class. For training, we provide solely
unlabeled images and start from random network initialization. For quantitative
and qualitative evaluation, we use established benchmarks, and COCO-Persons,
whereby we introduce the latter in this paper as a challenging novel benchmark.
InfoSeg significantly outperforms the current state-of-the-art, e.g., we
achieve a relative increase of 26% in the Pixel Accuracy metric on the
COCO-Stuff dataset.

本文提出了一种基于自监督学习的无监督语义图像分割的新方法，通过局部和全局高级别图像特征间的互信息最大化计算多个高级别特征，实现对不同语义类别的图像分割和互信息最大化的两阶段学习过程，并在已有数据集和引入的 COCO-Persons 数据集上相对推进了 26%（基于像素准确率评估）。

InfoSeg: 基于互信息最大化的无监督语义图像分割

InfoSeg: Unsupervised Semantic Image Segmentation with Mutual  Information Maximization

Recent results suggest that state-of-the-art saliency models perform far from
optimal in predicting fixations. This lack in performance has been attributed
to an inability to model the influence of high-level image features such as
objects. Recent seminal advances in applying deep neural networks to tasks like
object recognition suggests that they are able to capture this kind of
structure. However, the enormous amount of training data necessary to train
these networks makes them difficult to apply directly to saliency prediction.
We present a novel way of reusing existing neural networks that have been
pretrained on the task of object recognition in models of fixation prediction.
Using the well-known network of Krizhevsky et al. (2012), we come up with a new
saliency model that significantly outperforms all state-of-the-art models on
the MIT Saliency Benchmark. We show that the structure of this network allows
new insights in the psychophysics of fixation selection and potentially their
neural implementation. To train our network, we build on recent work on the
modeling of saliency as point processes.

本文介绍一种新颖的方法，通过重复使用深度神经网络的预训练模型，构建一种针对显著性预测的模型，从而有效地提高了预测准确性，并展示此网络结构可以为视觉注意机制的理解提供新的见解。