CPR++: Object Localization via Single Coarse Point Supervision

Abstract: Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance due to the inconsistency of annotated points. Existing POL heavily rely on strict annotation rules, which are difficult to define and apply, to handle the problem. In this study, we propose coarse point refinement (CPR), which to our best knowledge is the first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. Furthermore, We design a sampling region estimation module to dynamically compute a sampling region for each object and use a cascaded structure to achieve end-to-end optimization. We further integrate a variance regularization into the structure to concentrate the predicted scores, yielding CPR++. We observe that CPR++ can obtain scale information and further reduce the semantic variance in a global region, thus guaranteeing high-performance object localization. Extensive experiments on four challenging datasets validate the effectiveness of both CPR and CPR++. We hope our work can inspire more research on designing algorithms rather than annotation rules to address the semantic variance problem in POL. The dataset and code will be public at github.com/ucas-vg/PointTinyBenchmark.

• The paper proposes a novel MIL-based approach that refines single coarse point annotations to improve object localization accuracy.
• It introduces a dynamic sampling region strategy and variance regularization to effectively address semantic and scale variance.
• Experiments on COCO, DOTA, Pascal VOC, and SeaPerson demonstrate significant gains, achieving up to 59.08 mAP on COCO with ResNet-101.

Overview of CPR++: Object Localization via Single Coarse Point Supervision

The paper introduces CPR++ (Coarse Point Refinement Plus Plus), a novel approach to object localization using single coarse point supervision. The primary aim is to address the challenges of semantic variance in point-based object localization (POL), which traditionally relies on rigid annotation rules that are often cumbersome to define and implement.

The authors propose a methodology that reduces semantic variance not through annotation precision but through algorithmic refinement. CPR++, as an extension of Coarse Point Refinement (CPR), employs a multi-instance learning (MIL) paradigm to identify and utilize a semantic center point from a sampled region, improving the localization accuracy.

Methodology

The paper describes a three-step process in CPR++:

1. Coarse Point Annotation: Objects are annotated with any identifiable point, simplifying the annotation process.
2. Coarse Point Refinement (CPR): Initially, points within a neighborhood are sampled. Using MIL, a model infers which points likely represent object centers, thus refining the initial annotation.
3. Refined Point Localization with CPR++: Building on CPR, CPR++ dynamically estimates sampling regions and refines them iteratively in a cascade manner, incorporating variance regularization to focus predictions on more precise points.

Experimental Results

The paper includes extensive experiments validating CPR++ across four datasets: COCO, DOTA, Pascal VOC, and SeaPerson. Notably, CPR++ shows significant performance improvements in object localization compared to baselines, achieving up to 59.08 mAP on the COCO dataset using a ResNet-101 backbone. Particularly, CPR++ outperforms both the basic POL implementations and models trained with pseudo-bounding boxes.

Key Contributions

• Algorithmic Mitigation of Semantic Variance: By employing MIL and dynamically refining sampling regions, CPR++ reduces reliance on detailed and consistent manual annotations.
• Dynamic Sampling Region: CPR++ introduces an adaptive mechanism for estimating sampling regions, addressing the challenge of scale variance effectively.
• Broad Applicability: The approach is validated across multiple datasets, demonstrating robustness and scalability.

Implications and Future Work

The CPR++ model offers a promising direction by simplifying annotation processes, making it viable for real-world applications where efficiently labeled data might be sparse. The study could inspire further exploration into adaptive annotation-driven models in AI.

For future work, the paper suggests investigating additional ways to reduce computational overhead and extending the approach to other domains in AI where coarse annotations are beneficial.