Object Segmentation Using Receptive-Field Feature Map Cropping
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Solution Overview
Problem
Conventional object segmentation systems inefficiently process entire digital images and feature maps, leading to unnecessary computations and resource usage.
Innovation Solution
A segment system computes receptive fields for nodes of a machine learning model based on user-specified coordinates, tracing dependencies between layers to identify and process only relevant portions of the image for generating segment masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object segmentation systems process entire digital images and feature maps, then complete object segmentation can be achieved, but computation and memory resource usage increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the entire image processing task into smaller, focused computations. Instead of processing the complete image, the system computes receptive fields for specific nodes in the machine learning model and processes only the corresponding portions of the feature map, thereby segmenting the computational workload to reduce resource usage while maintaining segmentation accuracy.
Solution Approach 2:
The patent extracts only the necessary portions of the feature map for processing. By calculating receptive fields for specific nodes and identifying the corresponding image regions, the system extracts and processes only the relevant portions of the feature map rather than the entire feature map, significantly reducing memory and computation resource requirements.
2Loss of information
If conventional systems process entire digital images, then all object information is captured, but memory resource usage increases
Solution Approach 1:
The patent applies local quality by processing only the specific portions of the feature map corresponding to the receptive fields of interest. Instead of loading and processing the entire feature map into memory, the system identifies and processes only the local regions necessary for generating accurate segment masks, thereby reducing memory usage while preserving essential object information.
3Manufacturing precision
If receptive fields are computed for all nodes in machine learning model layers, then complete feature processing is achieved, but computation time increases
Solution Approach 1:
The patent applies partial action by computing receptive fields only for the necessary nodes in the machine learning model layers rather than all nodes. The system identifies and processes only the portions of the feature map corresponding to the computed receptive fields, achieving sufficient accuracy for segment mask generation without the computational overhead of processing the entire feature map.
Data Source
AI summary
In implementations of systems for efficient object segmentation, a computing device implements a segment system to receive a user input specifying coordinates of a digital image. The segment system computes receptive fields of a machine learning model based on the coordinates of the digital image. The machine learning model is trained on training data to generate segment masks for objects depicted in digital images. The segment system processes a portion of a feature map of the digital image using the machine learning model based on the receptive fields. A segment mask is generated for an object depicted in the digital image based on processing the portion of the feature map of the digital image using the machine learning model.