Foreground Background Reciprocal Attention Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing salient object segmentation methods rely solely on characterizing foreground features in images, neglecting the reciprocal attention between foreground and background, resulting in poor segmentation effects.
Innovation Solution
An image salient object segmentation method and apparatus based on reciprocal attention between foreground and background, utilizing a convolutional neural backbone network to obtain feature maps, calculating a reciprocal attention weight matrix, and updating feature responses to enhance foreground and background segmentation, with a cooperative loss function for improved prediction results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing segmentation methods only characterize foreground feature expression, then the method complexity remains relatively simple, but the segmentation precision deteriorates due to poor segmentation effect
Solution Approach 1:
The patent divides the segmentation task into two independent branches: foreground segmentation branch and background segmentation branch. Each branch has its own feature extraction and segmentation processes, allowing them to be optimized independently while improving overall segmentation precision through their complementary relationship.
Solution Approach 2:
The patent introduces background segmentation as an inverse problem to foreground segmentation. By training the network to segment both foreground and background simultaneously with opposite objectives, the model learns more discriminative features that improve foreground segmentation precision while maintaining manageable complexity.
2Measurement precision
If reciprocal attention mechanism is introduced between foreground and background, then the segmentation precision improves through complementary predictions, but the computational complexity increases
Solution Approach 1:
The patent merges the foreground and background segmentation tasks into a unified training framework with a shared backbone network. The cooperative loss function combines both segmentation objectives, allowing the model to learn complementary representations that improve precision while avoiding the need for separate independent models that would increase computational energy consumption.
3Measurement precision
If foreground and background are segmented independently without considering mutual relationship, then the training process is simpler, but the segmentation precision deteriorates due to lack of complementary information
Solution Approach 1:
The patent implements feedback between foreground and background segmentation through the cooperative loss function. The background segmentation results provide implicit feedback about foreground boundaries, and vice versa, allowing each branch to refine its predictions based on the other's performance, thereby improving overall segmentation precision.
Solution Approach 2:
The patent adds a new dimension to the segmentation problem by introducing background segmentation as a parallel task. This transforms the single-objective foreground segmentation into a multi-objective problem, where the background dimension provides complementary information that enhances foreground segmentation precision without requiring complex multi-scale or hierarchical architectures.
Data Source
AI summary
An image salient object segmentation method and an apparatus based on reciprocal attention between a foreground and a background, and the method includes: obtaining a feature map corresponding to a training image based on a convolutional neural backbone network, and obtaining foreground and background initial feature responses according to the feature map; obtaining a reciprocal attention weight matrix, and updating the foreground and background initial feature responses according to the reciprocal attention weight matrix, to obtain foreground and background feature maps; training the convolutional neural backbone network according to the foreground and the background feature maps based on a cross entropy loss function and a cooperative loss function, to obtain a foreground and background segmentation convolutional neural network model; and inputting an image to be segmented into the foreground and background segmentation convolutional neural network model to obtain foreground and background prediction results.