Corpus ID: 210965940

Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning

@article{Kim2020WeaklySI,
  title={Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning},
  author={Seohyun Kim and Jaedong Hwang and J. Son and Bohyung Han},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.11207}
}
We present an object segmentation algorithm based on community learning for multiple tasks under the supervision of image-level class labels only, where individual instances of the same class are identified and segmented separately. This problem is formulated as a combination of weakly supervised object detection and semantic segmentation, and is addressed by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression… Expand
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References

SHOWING 1-10 OF 46 REFERENCES
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing
TLDR
This paper proposes to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing, and obtains the state-of-the-art performance. Expand
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations
TLDR
IRNet is proposed, which estimates rough areas of individual instances and detects boundaries between different object classes and enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately. Expand
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
  • Jifeng Dai, Kaiming He, Jian Sun
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
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. Expand
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation
TLDR
This work designs a process to selectively collect pseudo supervision from noisy segment proposals obtained with previously published techniques and uses it to learn a differentiable filling module that predicts a class-agnostic activation map for each instance given the image and an incomplete region response. Expand
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
TLDR
This paper first shows that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then shows that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. Expand
Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
TLDR
A novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation, and a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms is proposed. Expand
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TLDR
This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Expand
Weakly Supervised Cascaded Convolutional Networks
TLDR
This work introduces two new architecture of cascaded networks, with either two cascade stages or three which are trained in an end-to-end pipeline to learn a convolutional neural network (CNN) under such conditions. Expand
Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network
TLDR
This work proposes Superpixel Pooling Network (SPN), which utilizes superpixel segmentation of input image as a pooling layout to reflect low-level image structure for learning and inferring semantic segmentation. Expand
Weakly Supervised Deep Detection Networks
  • Hakan Bilen, A. Vedaldi
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
This paper proposes a weakly supervised deep detection architecture that modifies one such network to operate at the level of image regions, performing simultaneously region selection and classification. Expand
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