Samsung electronics co., ltd. (20240177318). TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS simplified abstract
Contents
- 1 TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS
Organization Name
Inventor(s)
Mostafa El-khamy of San Diego CA (US)
Qingfeng Liu of San Diego CA (US)
TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177318 titled 'TEMPORAL SEMANTIC BOUNDARY LOSS FOR VIDEO SEMANTIC SEGMENTATION NETWORKS
Simplified Explanation
The patent application describes a method for semantic segmentation in a network using input data from multiple frames to compute ground truth labels, generate ground truth temporal semantic boundary maps, predict temporal semantic boundary maps, and determine loss based on the comparison between the ground truth and predicted maps.
- Receiving input data from multiple frames in a semantic segmentation network
- Computing ground truth labels on the frames
- Generating ground truth temporal semantic boundary maps from the labels
- Generating predicted temporal semantic boundary maps based on the input data output
- Determining loss based on the comparison of ground truth and predicted maps
Potential Applications
This technology could be applied in video analysis, autonomous driving systems, medical imaging, and surveillance systems.
Problems Solved
This technology helps improve the accuracy and efficiency of semantic segmentation in video processing tasks.
Benefits
The benefits of this technology include enhanced object recognition, improved scene understanding, and better performance in various applications.
Potential Commercial Applications
Potential commercial applications of this technology include video editing software, security systems, medical imaging software, and autonomous vehicles.
Possible Prior Art
Prior art in this field may include research papers, patents, or existing technologies related to semantic segmentation in video processing tasks.
Unanswered Questions
How does this method compare to existing techniques in terms of accuracy and efficiency?
The article does not provide a direct comparison with existing techniques in the field.
What are the computational requirements of implementing this method in real-time applications?
The article does not address the computational resources needed for real-time implementation of this method.
Original Abstract Submitted
disclosed is a method including receiving, in a semantic segmentation network, input data from a plurality of frames, computing a ground truth label on the plurality of frames, generating a ground truth temporal semantic boundary map from the ground truth label on the plurality of frames, generating a predicted temporal semantic boundary map based on an output of the input data, and determining a loss based on the ground truth temporal semantic boundary map and the predicted temporal semantic boundary map.
