You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. This is challenging to solve as encountering the extreme boundary non-boundary class imbalance during training an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoder-decoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the state-of-the-art on the PIOD dataset (ODS F-score of .702) and the BSDS ownership dataset (ODS F-score of .555), as well as improving the detecting speed to as 0.037s per image on the PIOD dataset.

Cited Abstract(s):
[1]: In this paper, we propose a deep convolutional network architecture, called DOC, which detects object boundaries and estimates the occlusion relationships (i.e. which side of the boundary is foreground and which is background). Specifically, we first represent occlusion relations by a binary edge indicator, to indicate the object boundary, and an occlusion orientation variable whose direction specifies the occlusion relationships by a left-hand rule, see Fig. 1. Then, our DOC networks exploit local and non-local image cues to learn and estimate this representation and hence recover occlusion relations. To train and test DOC, we construct a large-scale instance occlusion boundary dataset using PASCAL VOC images, which we call the PASCAL instance occlusion dataset (PIOD). It contains 10,000 images and hence is two orders of magnitude larger than existing occlusion datasets for outdoor images. We test two variants of DOC on PIOD and on the BSDS ownership dataset and show they outperform state-of-the-art methods typically by more than 5AP. Finally, we perform numerous experiments investigating multiple settings of DOC and transfer between BSDS and PIOD, which provides more insights for further study of occlusion estimation.
Similar to DOC [1] , our method detects object boundaries and estimates the occlusion relationships from a single image, which is referred as the object occlusion boundary detection. Notably, we adapt a single stream network architecture simultaneously predicting both object boundary and occlusion orientation in a single step by sharing convolutional features.