当前位置: X-MOL 学术IEEE Trans. Pattern Anal. Mach. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Self-Supervised Human Detection and Segmentation via Background Inpainting.
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2022-11-07 , DOI: 10.1109/tpami.2021.3123902
Isinsu Katircioglu , Helge Rhodin , Victor Constantin , Jorg Sporri , Mathieu Salzmann , Pascal Fua

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. At the heart of our approach lies the observation that object segmentation and background reconstruction are linked tasks, and that, for structured scenes, background regions can be re-synthesized from their surroundings, whereas regions depicting the moving object cannot. We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of the proposals, we develop a Monte Carlo-based training strategy that allows the algorithm to explore the large space of object proposals. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.

中文翻译:

通过背景修复进行自我监督的人体检测和分割。

虽然受监督的对象检测和分割方法取得了令人印象深刻的准确性,但它们对外观与训练数据有显着差异的图像的泛化能力很差。为了在注释数据的成本过高时解决这个问题,我们引入了一种自我监督的检测和分割方法,该方法可以处理可能移动的相机捕获的单个图像。我们方法的核心在于观察到对象分割和背景重建是相互关联的任务,并且对于结构化场景,背景区域可以从周围环境中重新合成,而描绘运动对象的区域则不能。我们将这种直觉编码成一个自我监督的损失函数,我们利用它来训练基于提议的分割网络。考虑到提案的离散性,我们开发了一种基于蒙特卡罗的训练策略,允许算法探索大空间的对象建议。我们将我们的方法应用于图像中的人体检测和分割,这些图像在视觉上偏离了标准基准,并且优于现有的自我监督方法。
更新日期:2021-10-29
down
wechat
bug