Object co-segmentation is to segment the shared objects in multiple relevant
images, which has numerous applications in computer vision. This paper presents
a spatial and semantic modulated deep network framework for object
co-segmentation. A backbone network is adopted to extract multi-resolution
image features. With the multi-resolution features of the relevant images as
input, we design a spatial modulator to learn a mask for each image. The
spatial modulator captures the correlations of image feature descriptors via
unsupervised learning. The learned mask can roughly localize the shared
foreground object while suppressing the background. For the semantic modulator,
we model it as a supervised image classification task. We propose a
hierarchical second-order pooling module to transform the image features for
classification use. The outputs of the two modulators manipulate the
multi-resolution features by a shift-and-scale operation so that the features
focus on segmenting co-object regions. The proposed model is trained end-to-end
without any intricate post-processing. Extensive experiments on four image
co-segmentation benchmark datasets demonstrate the superior accuracy of the
proposed method compared to state-of-the-art methods.

本文提出了一个空间和语义调制的深度网络框架，用于共同分割多个相关图像中的共享对象。该模型通过使用骨干网络提取多分辨率的图像特征，并通过非监督学习捕获图像特征描述符之间的相关性。使用监督图像分类任务来模拟语义调制器，并使用层次二次池化模块转换图像特征，以集中于共同对象区域的分割。结果表明，该模型在四个图像共分割基准数据集上的表现优于现有的最先进方法。

基于空间语义网络调制的深层物体共分割

Deep Object Co-segmentation via Spatial-Semantic Network Modulation

We present an approach for jointly matching and segmenting object instances
of the same category within a collection of images. In contrast to existing
algorithms that tackle the tasks of semantic matching and object
co-segmentation in isolation, our method exploits the complementary nature of
the two tasks. The key insights of our method are two-fold. First, the
estimated dense correspondence fields from semantic matching provide
supervision for object co-segmentation by enforcing consistency between the
predicted masks from a pair of images. Second, the predicted object masks from
object co-segmentation in turn allow us to reduce the adverse effects due to
background clutters for improving semantic matching. Our model is end-to-end
trainable and does not require supervision from manually annotated
correspondences and object masks. We validate the efficacy of our approach on
five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k,
and show that our algorithm performs favorably against the state-of-the-art
methods on both semantic matching and object co-segmentation tasks.

我们提出了一种联合匹配和分割图像集合中同一类别的对象实例的方法，这种方法利用了两个任务的互补性，通过从语义匹配得到的估计密集对应场来提供对象共分割的监督，并且从对象共分割得到的预测对象掩码来改善语义匹配的性能，我们的算法在五个基准数据集上表现优异。

联合弱监督学习语义匹配和物体共分割：展示、匹配和分割

Show, Match and Segment: Joint Weakly Supervised Learning of Semantic  Matching and Object Co-segmentation

Object co-segmentation is the task of segmenting the same objects from
multiple images. In this paper, we propose the Attention Based Object
Co-Segmentation for object co-segmentation that utilize a novel attention
mechanism in the bottleneck layer of deep neural network for the selection of
semantically related features. Furthermore, we take the benefit of attention
learner and propose an algorithm to segment multi-input images in linear time
complexity. Experiment results demonstrate that our model achieves state of the
art performance on multiple datasets, with a significant reduction of
computational time.

使用基于注意力机制的深度神经网络提出了面向对象共分割的注意力机制，同时使用注意力学习者算法实现了线性时间复杂度的多输入图像分割，实验结果表明，该模型在多个数据集上取得了最先进的性能，并大幅降低了计算时间。