Probabilistic two-stage detection (2103.07461v1)

Abstract: We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp, outperforming the popular YOLOv4 model.

• The paper introduces a probabilistic framework unifying objectness and classification scores to enhance likelihood calibration in two-stage detectors.
• It presents a novel architecture that improves detection accuracy to 56.4 mAP on COCO test-dev and achieves 49.2 mAP at 33 fps with a lightweight backbone.
• Extensive experiments on diverse datasets confirm the method's robustness and versatility, setting new benchmarks for both speed and precision in object detection.

Probabilistic Two-Stage Detection: A Comprehensive Analysis

In the paper "Probabilistic Two-Stage Detection," the authors present a nuanced approach to interpreting two-stage object detection through a probabilistic lens. Their work builds a bridge between empirical training methodologies and theoretical probabilistic constructs, suggesting a reorientation in the architecture and training of two-stage detectors for enhanced performance.

The crux of the paper lies in the probabilistic reinterpretation of the two-stage detection process, which traditionally lacks an integrated probabilistic foundation across both stages. The paper delineates the importance of the first stage in generating calibrated object-vs-background likelihoods, a task for which typical Region Proposal Networks (RPNs) fall short. Instead, the authors leverage one-stage detectors known for their probabilistic soundness in generating such likelihoods.

Subsequently, the authors propose a methodological transition by constructing a probabilistic two-stage detector hinged on state-of-the-art one-stage detectors. This novel architecture not only redefines the computational workflow of the detection pipeline but also yields superior speed and accuracy. Empirical evidence supports this claim, as their detectors achieve a mAP of 56.4 on COCO test-dev with single-scale testing, setting a new benchmark over existing models. Moreover, with a lightweight backbone, the detector reaches 49.2 mAP at 33 fps, surpassing the performance of the well-regarded YOLOv4 model on equivalent hardware.

Key Contributions and Results

1. Probabilistic Modeling: The paper introduces a probabilistic treatment of two-stage detectors, unifying the objectness score from the first stage with the conditional classification score from the second stage to provide a joint probabilistic objective. This approach enhances the calibration of detection scores across stages.
2. Enhanced Detection Accuracy and Speed: By incorporating a probabilistic model, the proposed detectors improve on both traditional two-stage and single-stage precursors. Specific numerical results underscore the strength of this approach, with improvements in mAP ranging from 1 to 3 percentage points across various configurations and datasets. The detectors are also faster due to a reduction in proposal count and improved feature extraction efficiencies.
3. Versatility Across Datasets: Experiments conducted on datasets with varying volumes and vocabularies such as COCO, LVIS, and Objects365 demonstrate the framework's robustness and adaptability in handling large-scale and diverse categories with enhanced detection precision.
4. Stage Integration via a Probabilistic Framework: The integration of efficient one-stage detector processes into a two-stage framework culminates in a system that both benefits from and improves upon the advances in single-stage detection methodologies.

Implications and Future Directions

The research has laid foundational work for further advancements in object detection, particularly in integrating probabilistic methods for more coherent detection pipelines. Practically, this approach could revolutionize real-time applications requiring rapid and accurate object detection, including autonomous systems and large-scale surveillance.

Theoretically, the probabilistic framework offers a compelling avenue for future research, emphasizing the importance of likelihood calibration and probabilistic compositionality in detection tasks. Future work could expand upon this by exploring varied probabilistic distributions and likelihood estimation methods at different detection stages.

Overall, this paper offers a significant contribution to object detection research, reflecting both in its methodological innovation and in its promising experimental outcomes. The probabilistic two-stage detection methodology not only improves current practices but also paves the way for more robust, unified detection frameworks that foster improved detection accuracy while maintaining computational efficiency.