Salient Object Ranking With Real-Time Saliency Map Generation
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Solution Overview
Problem
Existing saliency detection methods struggle with accurately localizing salient objects in images, requiring large numbers of class-agnostic proposals and pixel-level annotations, leading to inefficiencies and poor performance in real-time applications.
Innovation Solution
An end-to-end deep network for real-time salient object detection (RSD) that learns to generate a saliency map with the exact number of salient objects using Gaussian distributions centered at ground-truth objects, eliminating the need for pixel-level annotations and object proposals, and enabling simultaneous salient object detection in a single pass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If salient foreground segmentation is used to detect salient objects, then pixel-level annotation accuracy is improved, but the complexity and cost of acquiring annotations increases significantly
Solution Approach 1:
The patent extracts and eliminates the need for pixel-level annotations by using bounding box annotations instead. The method processes only the bounding box labels and corresponding image regions, removing the complex pixel-level annotation requirement while maintaining object detection accuracy through a two-stage approach using proposal networks and refinement networks.
Solution Approach 2:
The patent changes the annotation parameter from pixel-level labels to bounding box labels. This parameter change simplifies the annotation process significantly while providing sufficient information for accurate salient object detection through the proposed network architecture that processes bounding boxes and generates saliency maps.
2Reliability
If generic object proposals are used to cover the whole image, then detection coverage is improved, but the number of redundant proposals increases significantly
Solution Approach 1:
The patent segments the proposal generation process into two distinct stages: a proposal network that generates initial candidate bounding boxes covering the whole image, and a refinement network that processes only the relevant proposals. This segmentation allows comprehensive coverage while reducing redundant proposals by filtering and refining only the necessary candidates.
Solution Approach 2:
The patent applies partial action by processing only the necessary proposals through the refinement stage rather than all generated proposals. The proposal network generates a comprehensive set, but the refinement network selectively processes only the relevant portion, reducing computational redundancy while maintaining detection coverage.
3Measurement precision
If additional refinement steps are applied to improve accuracy, then detection precision is improved, but the processing time increases making real-time applications infeasible
Solution Approach 1:
The patent merges the proposal generation and refinement functions into a unified two-stage network architecture where the proposal network and refinement network work together in an integrated manner. This combining allows the system to achieve high accuracy through coordinated processing while maintaining real-time performance by avoiding separate sequential refinement steps.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining a continuous flow of image data through the proposal network and refinement network in a single pass processing. The system continuously generates and refines proposals simultaneously rather than using discrete sequential steps, achieving both accuracy and real-time performance.
4Reliability
If a large number of class-agnostic proposals are generated, then the completeness of object detection is improved, but the computational overhead increases significantly
Solution Approach 1:
The patent applies preliminary action by using the proposal network to pre-generate a comprehensive set of candidate bounding boxes before the refinement stage. This preliminary generation ensures completeness by covering the whole image, while the refinement network then processes only the necessary proposals to reduce computational overhead for the main detection task.
Data Source
AI summary
Methods, systems, and computer programs are presented for adding new features to a network service. An example method includes accessing an image from a user device to determine a salient object count of a plurality of objects in the image. A salient object count of the plurality of objects in the image is determined. An indicator of the salient object count of the plurality of objects in the image is caused to be displayed on the user device.