CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection (2003.09119v1)

Abstract: Keypoint-based detectors have achieved pretty-well performance. However, incorrect keypoint matching is still widespread and greatly affects the performance of the detector. In this paper, we propose CentripetalNet which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned. Combining position information, our approach matches corner points more accurately than the conventional embedding approaches do. Corner pooling extracts information inside the bounding boxes onto the border. To make this information more aware at the corners, we design a cross-star deformable convolution network to conduct feature adaption. Furthermore, we explore instance segmentation on anchor-free detectors by equipping our CentripetalNet with a mask prediction module. On MS-COCO test-dev, our CentripetalNet not only outperforms all existing anchor-free detectors with an AP of 48.0% but also achieves comparable performance to the state-of-the-art instance segmentation approaches with a 40.2% MaskAP. Code will be available at https://github.com/KiveeDong/CentripetalNet.

• The paper presents a centripetal shift method that realigns corner points to significantly reduce erroneous keypoint pairing in object detection.
• It integrates cross-star deformable convolutions to adapt features and enhance corner pooling, addressing limitations of traditional methods.
• Empirical results on MS-COCO report a 48.0% AP and a 40.2% Mask AP, demonstrating CentripetalNet's superior performance over existing techniques.

CentripetalNet: Advancements in Keypoint Pairing for Object Detection

This paper presents CentripetalNet, a novel approach to enhancing the accuracy of keypoint-based object detectors by addressing a critical challenge: incorrect keypoint matching. The authors identify the widespread issue of erroneous pairing in conventional keypoint detection methods and propose an innovative solution using a centripetal shift. CentripetalNet predicts both the position and centripetal shift of corner points, aligning the shifted corners to accurately pair them. This paper offers a rigorous analysis, quantitative results, and presents compelling evidence of CentripetalNet's superiority over traditional keypoint matching techniques.

Contributions and Methodological Innovations

The conventional methods often utilize associative embeddings to pair corners by learning additional embeddings for each corner. However, these methods are susceptible to errors, especially in scenarios with multiple similar objects or large geometric variances. CentripetalNet introduces the concept of a centripetal shift—defined as a vector encoding spatial offsets from the corner to the center of the bounding box. This approach significantly reduces the sensitivity to outliers and enhances robustness across various object scales.

To further boost feature adaption at corners, a novel component, cross-star deformable convolution, is integrated. This module adapts features to improve corner pooling outputs, addressing previous inefficiencies in feature representation of corner-based extractions. Cross-star deformable convolutions are adept at capturing both the large receptive fields and geometric structures necessary when aligning features at corner locations.

Performance and Impact

CentripetalNet's empirical evaluation showcases its proficiency. On the MS-COCO test-dev benchmark, it achieved an Average Precision (AP) of 48.0% for object detection, outperforming all existing anchor-free detectors. The Mask AP reached 40.2%, demonstrating competitiveness alongside state-of-the-art instance segmentation techniques. This two-pronged verification strongly supports CentripetalNet's claim of advancing both detection and segmentation tasks.

Implications and Future Directions

The introduction of CentripetalNet has significant implications for future research in object detection and beyond. The centripetal shift method can inspire further exploration into the integration of spatial and geometric information for feature alignment and model robustness. It also opens avenues for developing more sophisticated feature adaption techniques that transcend traditional associative embedding methodologies.

Practically, CentripetalNet's approach could be adapted to various other computer vision tasks, such as human pose estimation and object tracking, where precise keypoint pairing is crucial. The mask prediction module further extends its application potential into tasks demanding precise localization, such as automated medical imaging diagnostics.

Concluding Remarks

The contribution of CentripetalNet is notable for its methodological innovation and practical advancements in object detection accuracy. By strategically addressing the limitations of associative embedding methods, CentripetalNet makes a substantial step forward in the keypoint-based object detection landscape. As researchers continue to push the boundaries of AI and machine learning in computer vision, CentripetalNet serves as a foundation for future innovations aimed at achieving higher precision and adaptability in complex visual environments.