A deep framework for automatic annotation with application to retail warehouses

Keyphrases

• Training Time 100%
• Retail 100%
• Segmentation Accuracy 100%
• Annotated Dataset 100%
• Automatic Annotation 100%
• Warehouse 100%
• Amazon Robotics Challenge 100%
• Application Usage 50%
• Existing State 50%
• Convolutional Neural Network 50%
• New Dataset 50%
• Deep Learning Framework 50%
• Manual Labor 50%
• Automatically Generate 50%
• Multi-class Semantic Segmentation 50%
• Automatic Segmentation 50%
• Princeton 50%
• Residual Network 50%
• Amount of Training 50%
• Proposed Architecture 50%
• PSPNet 50%
• Residual Features 50%
• High-level Features 50%
• Primitive Features 50%
• Machine-generated 50%
• Mask R-CNN 50%
• Deep Learning Architectures 50%
• Deep Network 50%

Computer Science

• Convolutional Neural Network 100%
• Automatic Annotation 100%
• segmentation accuracy 100%
• Deep Learning 50%
• Use Case 50%
• Image Segmentation 50%
• Learning Framework 50%
• Residual Neural Network 50%
• Deep Architecture 50%
• Automatic Segmentation 50%
• Successive Layer 50%