SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond

@article{Ke2021SRNSR,
  title={SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond},
  author={Wei Ke and Jie Chen and Jianbin Jiao and Guoying Zhao and Qixiang Ye},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2021},
  volume={32},
  pages={1881-1895}
}
  • Wei KeJie Chen Qixiang Ye
  • Published 17 July 2018
  • Computer Science
  • IEEE Transactions on Neural Networks and Learning Systems
This article establishes a baseline for object reflection symmetry detection in natural images by releasing a new benchmark named Sym-PASCAL and proposing an end-to-end deep learning approach for reflection symmetry. Sym-PASCAL spans challenges of multiobjects, object diversity, part invisibility, and clustered backgrounds, which is far beyond those in existing data sets. The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual… 
3 Citations

A Method to Improve the Accuracy of Pavement Crack Identification by Combining a Semantic Segmentation and Edge Detection Model

A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed and is better than other detection methods based on deep learning.

Image Co-Skeletonization via Co-Segmentation

This article explores a new joint processing topic: image co-skeletonization, which is defined as joint skeleton extraction of the foreground objects in an image collection, and proposes a coupled framework for co-Skeletonization and co-segmentation tasks to facilitate shape information discovery for this process through the co- Segmentation process.

Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Earthquake detection and phase picking play a fundamental role in studying seismic hazards and the Earth’s interior. Many deep‐learning‐based methods, including the state‐of‐the‐art model called

References

SHOWING 1-10 OF 57 REFERENCES

SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild

A new benchmark and an end-to-end deep learning approach are presented, opening up a promising direction for symmetry detection in the wild, and the proposed symmetry detection approach, named Side-output Residual Network (SRN), exploits the flow of errors among multiple scales to ease the problems of fitting complex outputs with limited layers.

Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

A fully convolutional network with multiple scale-associated side outputs is presented to address object skeleton extraction in natural images, and achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.

Holistically-Nested Edge Detection

  • Saining XieZ. Tu
  • Computer Science
    2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
HED performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets, and automatically learns rich hierarchical representations that are important in order to resolve the challenging ambiguity in edge and object boundary detection.

RSRN: Rich Side-Output Residual Network for Medial Axis Detection

A Rich Side-output Residual Network (RSRN) for medial axis detection for ICCV 2017 workshop challenge on detecting symmetry in the wild using the rich features of fully convolutional network to decrease the residual between the detection result and the ground-truth.

DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection

This work shows that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs), while rather than using the networks as a blackbox feature extractor, it customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters.

Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

This work constructed the first deeplearning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmetry-labels from more than 400 human observers, and demonstrates abilities to identify viewpoint-varied 3D symmetries, partially occluded symmetrical objects, and symmetry at a semantic level.

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi- scale object detection, which is learned end-to-end, by optimizing a multi-task loss.

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

  • Jifeng DaiKaiming HeJian Sun
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
This paper presents Multitask Network Cascades for instance-aware semantic segmentation, which consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects, and develops an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.

2017 ICCV Challenge: Detecting Symmetry in the Wild

This report provides a detailed summary of the evaluation methodology for each type of symmetry detection algorithm validated, and demonstrates and analyzes quantified detection results in terms of precision-recall curves and F-measures for all algorithms evaluated.
...