RepPoints: Point Set Representation for Object Detection (1904.11490v2)

Abstract: Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present \textbf{RepPoints} (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 $AP_{50}$ on the COCO test-dev detection benchmark, using ResNet-101 model. Code is available at https://github.com/microsoft/RepPoints.

• The paper introduces a novel point set representation that replaces rigid bounding boxes with adaptive points for finer object localization.
• The paper demonstrates enhanced performance with an AP of 46.5 on COCO using ResNet-101, matching state-of-the-art methods.
• The paper shows that training RepPoints via joint localization and recognition significantly improves feature extraction and detection reliability.

RepPoints: Point Set Representation for Object Detection

The paper entitled "RepPoints: Point Set Representation for Object Detection" presents a novel representation scheme for object detection mechanisms that traditionally rely on rectangular bounding boxes for identifying objects within images. This new approach, called "RepPoints," models objects using a set of adaptive sample points intended to provide finer localization and enhanced feature extraction.

Summary

Conventionally, object detection pipelines employ rectangular bounding boxes as their basic geometric representation. While these bounding boxes facilitate feature extraction and alignment in deep neural networks, they provide a coarse and often insufficient representation by including significant non-object areas from the background or low-importance regions.

To address these limitations, RepPoints offers an alternative by modeling sample points scattered across the spatial extent of an object. These points are learned during training to adaptively position themselves in a manner that circumscribes an object's boundaries and indicates areas of semantic importance. The resulting RepPoints are shown to offer fine-grained localization and can be integrated coherently into existing multi-stage object detection pipelines without requiring anchors or any additional hand-crafted processing modules.

Key Findings

1. Flexible Object Representation: RepPoints represent objects as a dynamic set of points, in contrast to the static and rigid bounding box approach. The flexibility of this representation is evident through its successful application in anchor-free detection systems while providing competitive performance metrics.
2. Enhanced Performance: Utilizing the RepPoints method contributes to significant improvements in object detection performance. For instance, when using the ResNet-101 model on the COCO benchmark, the RepPoints-based detector achieves an Average Precision (AP) of 46.5 and an AP$_{50}$ of 67.4. This matches or exceeds state-of-the-art anchor-based detection methods.
3. Improved Localization and Feature Extraction: The training of RepPoints is driven not only by localization supervision but also by implicit recognition feedback. This ensures that the learned points closely align with the object's boundaries and salient regions, enhancing the quality of the localized features for better object classification.

Implications

From a theoretical standpoint, the transition from bounding boxes to point-based representations marks a notable conceptual shift in object detection techniques. It challenges the decades-long reliance on rectilinear models and suggests that objects can be better identified through a distribution of salient points rather than crude bounding constraints. This flexibility in object representation opens new avenues of research in computer vision and the design of more natural and adaptive object detectors.

Practically, the adoption of RepPoints promises to simplify the object detection process by obviating the need for complex configurations and tuning generally required in anchor-based systems. The thorough empirical analyses by the authors underscore the utility of RepPoints in terms of both efficiency and detection accuracy. Additionally, the anchor-free nature of RepPoints potentially reduces computational overhead and simplifies the design of object detection models, which can be especially beneficial for real-time applications on resource-constrained devices.

Future Directions

Several intriguing avenues for further exploration arise from this work. The authors suggest that learning even richer and more versatile point-based representations could be investigated. For instance, integrating more contextual and temporal information into the points may yield new capabilities in regions like video object detection, where the shape and position of objects change dynamically over time.

Moreover, the complementarity with geometric feature extraction methods such as deformable convolution reveals the potential for hybrid models that incorporate both point-based representations and flexible convolutional mechanisms to maximize detection performance. Further research could also explore the applicability of RepPoints to other domains beyond object detection, such as image segmentation and pose estimation.

Conclusion

RepPoints propose a sophisticated and flexible paradigm for object detection, emphasizing fine-grained object description via adaptable sample points. This approach not only enhances localization accuracy but also integrates seamlessly with multi-stage detection frameworks without necessitating anchors. Given its foundational shift in object representation, RepPoints highlight a promising direction for future advancements in computer vision.