Simple does it: weakly supervised instance and semantic segmentation
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This document discusses semantic segmentation using weak supervision from bounding boxes. It presents an approach called "Simple Does It" that trains a segmentation network on bounding box annotations instead of full pixel-level labels. The network learns to generate segmentation masks and classify object categories within bounding boxes. Related work on weakly supervised segmentation is also mentioned. Evaluation shows the approach achieves 20% accuracy compared to human labels, and combining with GrabCut post-processing improves results further.
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Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
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Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
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Vitthal Shirke Microservices Resume Montevideo
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Simple does it: weakly supervised instance and semantic segmentation
- 1. Semantic Segmentation with Bounding Box Member 1: 呂承祐 Member 2: 黃宇晟
- 2. Outline Problem setup Simple Does It: Weakly Supervised Instance and Semantic Segmentation Related work Reference
- 3. Problem setup Fully Supervised Segmentation Model Need a large number of training samples for high quality results Creating large segmentation training sets are expensive
- 4. One method Weakly Supervised Semantic Segmentation Bounding box Semantic SegmentationNeural Network Noise Label
- 5. Simple Does It: Weakly Supervised Instance and Semantic Segmentation Author: Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele Publish: arXiv Date: 23 Nov 2016
- 6. Image bbox Segmentation network bbox’ C1：background C2：object extend C3：objectness Post processing Ex: deepLab Ground Truth NaïveBox 20% 𝐁𝐨𝐱 𝑖 GrabCut+ GrabCut+M Ո G+ M Ո G+
- 7. Performance Image GrabCut+ M Ո G+ Human labelled
- 8. Performance
- 9. Reference Simple Does It: Weakly Supervised Instance and Semantic Segmentation