RepPoints V2: Verification Meets Regression for Object Detection (2007.08508v1)

Abstract: Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to continuous target variables. Hence, it is often beneficial to carefully combine them to take advantage of their benefits. In this paper, we take this philosophy to improve state-of-the-art object detection, specifically by RepPoints. Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement. We introduce verification tasks into the localization prediction of RepPoints, producing RepPoints v2, which provides consistent improvements of about 2.0 mAP over the original RepPoints on the COCO object detection benchmark using different backbones and training methods. RepPoints v2 also achieves 52.1 mAP on COCO \texttt{test-dev} by a single model. Moreover, we show that the proposed approach can more generally elevate other object detection frameworks as well as applications such as instance segmentation. The code is available at https://github.com/Scalsol/RepPointsV2.

• The paper introduces a fusion of verification and regression tasks to enhance object detection accuracy.
• It employs corner and within-box verification to enrich feature representation and improve precise localization.
• Experiments on the COCO benchmark show an approximate 2.0 mAP boost, validating the advanced performance of the approach.

An Analysis of "RepPoints v2: Verification Meets Regression for Object Detection"

The paper "RepPoints v2: Verification Meets Regression for Object Detection" proposes an innovative methodology to enhance object detection accuracy by strategically fusing verification tasks with regression. This work is an extension of the RepPoints framework, which originally relied heavily on regression for object localization. While traditional methods, such as Faster R-CNN and RetinaNet, utilized a combination of verification and regression, recent advancements allowed pure regression-based methods like the original RepPoints to achieve competitive results.

Overview of the Approach

RepPoints v2 introduces verification tasks into the regression framework of RepPoints to harness the strengths of both methodologies. Specifically, the tasks involve:

1. Corner Point Verification: This task verifies whether points on the feature map correspond to the corners of an object bounding box. It draws inspiration from CornerNet and incorporates focal loss for corner verification.
2. Within-Box Foreground Verification: This ensures that the points within an object's bounding box are correctly identified as part of the object. Unlike corner verification, this task aims to provide localization data across the entire bounding box.

Fusion Methodology

The proposed approach creatively utilizes auxiliary side-branches for the verification processes, allowing these tasks to be integrated without disrupting the primary regression-based workflow. The integration points include:

• Feature-Level Fusion: The output from verification tasks is fused into the major regression branch to improve feature representation through multi-task learning. This does not require additional annotations beyond the bounding box, unlike methods such as Mask R-CNN.
• Joint Inference: Combines verification scores with regression outputs for refined bounding box predictions, particularly improving the precision in localization.

Experimental Evaluation

The effectiveness of RepPoints v2 is substantiated by statistically significant improvements in detection accuracy. The paper reports a consistent gain of approximately 2.0 mAP over the original framework when evaluated on the COCO object detection benchmark. With a ResNeXt-101-DCN backbone, RepPoints v2 achieves a notable 52.1 mAP, demonstrating the impact of incorporating verification tasks. Furthermore, these results validate that the proposed fusion approach is generalizable, as similar improvements were achieved with other systems like FCOS, and in applications like instance segmentation.

Implications and Future Prospects

The paper’s contributions lie in the novel fusion of verification with regression, which marks a step forward in object detection frameworks. This approach may inspire subsequent research to explore other avenues of multi-method integration, potentially outside object detection, in areas that could benefit from similar synergies.

Looking ahead, the implications of this research suggest avenues for exploring additional forms of verification, potentially incorporating semantic or contextual information to further enhance detection accuracy. Future developments could focus on optimizing the efficiency of such frameworks to maintain the computational feasibility in real-time applications.

In conclusion, this research provides a robust framework for improving object detection by effectively marrying verification with regression. It extends the efficacy of the RepPoints methodology and sets a precedent for further innovations in hybrid detection strategies.