CPR++: Object Localization via Single Coarse Point Supervision.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Point-based object localization (POL) aims for efficient object sensing with minimal annotation costs.
• Existing POL methods struggle with semantic variance caused by inconsistent point annotations.
• Current solutions rely on complex, difficult-to-apply annotation rules.

Purpose of the Study:

• To introduce an algorithmic approach, Coarse Point Refinement (CPR), to mitigate semantic variance in POL.
• To develop a novel method that reduces reliance on strict annotation guidelines.
• To enhance the robustness and accuracy of point-based object localization.

Main Methods:

• Proposing Coarse Point Refinement (CPR) to replace initial points with semantic center points within a neighborhood.
• Designing a sampling region estimation module for dynamic, object-specific sampling areas.
• Implementing a cascaded structure for end-to-end optimization and integrating variance regularization (CPR++).

Main Results:

• CPR effectively reduces semantic variance by refining annotated points algorithmically.
• CPR++ demonstrates improved performance by incorporating scale information and global variance reduction.
• Experiments on four datasets confirm the significant effectiveness of both CPR and CPR++.

Conclusions:

• CPR offers a promising algorithmic solution to the semantic variance problem in POL.
• CPR++ achieves high-performance object localization by further refining semantic variance and capturing scale information.
• This work encourages algorithmic innovation over annotation rule-based approaches for POL challenges.