[计算机视觉论文速递] 2018-07-07 CVPR 图像分割专场1

导言

这篇文章有 2篇论文速递，都是图像分割方向（CVPR 2018），一篇提出CCB-Cut损失，另一篇是对FCN网络进行了改进。注意，两篇都是CVPR 2018文章。

编辑: Amusi

校稿: Amusi

时间: 2018-07-07

前戏

Amusi 将日常整理的论文都会同步发布到 daily-paper-computer-vision 上。名字有点露骨，还请见谅。喜欢的童鞋，欢迎star、fork和pull。

直接点击“阅读全文”即可访问daily-paper-computer-vision。

link: https://github.com/amusi/daily-paper-computer-vision

图像分割（Image Segmentation）

《Compassionately Conservative Balanced Cuts for Image Segmentation》

CVPR 2018

Abstract：The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segments that are balanced towards having lower values than unbalanced partitionings. However, this bias is so strong that it avoids any singleton partitions, even when vertices are very weakly connected to the rest of the graph. Motivated by the B\"uhler-Hein family of balanced cut costs, we propose the family of Compassionately Conservative Balanced (CCB) Cut costs, which are indexed by a parameter that can be used to strike a compromise between the desire to avoid too many singleton partitions and the notion that all partitions should be balanced. We show that CCB-Cut minimization can be relaxed into an orthogonally constrained ℓτ-minimization problem that coincides with the problem of computing Piecewise Flat Embeddings (PFE) for one particular index value, and we present an algorithm for solving the relaxed problem by iteratively minimizing a sequence of reweighted Rayleigh quotients (IRRQ). Using images from the BSDS500 database, we show that image segmentation based on CCB-Cut minimization provides better accuracy with respect to ground truth and greater variability in region size than NCut-based image segmentation.

arXiv：https://arxiv.org/abs/1803.09903

注：上述没有翻译！Amusi觉得这段英文读起来更舒服（绝不是偷懒哦）

《Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation》

CVPR 2018

Illustration of quantization framework based on the suggestive annotation framework

Uncertainty comparison between suggestive annotation and suggestive annotation with quantization

Abstract：With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagno- sis, and medical intervention. Since manual anno- tation su ers limited reproducibility, arduous e orts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural networks (DNNs), par- ticularly fully convolutional networks (FCNs), have been widely applied to biomedical image segmenta- tion, attaining much improved performance. At the same time, quantization of DNNs has become an ac- tive research topic, which aims to represent weights with less memory (precision) to considerably reduce memory and computation requirements of DNNs while maintaining acceptable accuracy. In this paper, we apply quantization techniques to FCNs for accurate biomedical image segmentation. Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy. Speci cally, we focus on a state-of- the-art segmentation framework, suggestive annotation [22], which judiciously extracts representative annota- tion samples from the original training dataset, obtain- ing an e ective small-sized balanced training dataset. We develop two new quantization processes for this framework: (1) suggestive annotation with quantiza- tion for highly representative training samples, and (2) network training with quantization for high accuracy. Extensive experiments on the MICCAI Gland dataset show that both quantization processes can improve the segmentation performance, and our proposed method exceeds the current state-of-the-art performance by up to 1%. In addition, our method has a reduction of up to 6.4x on memory usage.

arXiv：https://arxiv.org/abs/1712.00433

注：之后会推出该论文的精读文章！