Region Proposal by Guided Anchoring (1901.03278v2)

Abstract: Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. In this paper, we revisit this foundational stage. Our study shows that it can be done much more effectively and efficiently. Specifically, we present an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring. The proposed method jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations. On top of predicted anchor shapes, we mitigate the feature inconsistency with a feature adaption module. We also study the use of high-quality proposals to improve detection performance. The anchoring scheme can be seamlessly integrated into proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively improving the detection mAP by 2.2%, 2.7% and 1.2%. Code will be available at https://github.com/open-mmlab/mmdetection.

An Analysis of "Region Proposal by Guided Anchoring"

The paper "Region Proposal by Guided Anchoring" introduces an enhancement in the fundamental stage of region anchors within object detection frameworks. Traditional object detectors employ a dense anchoring scheme where anchors are uniformly distributed across spatial domains with predefined scales and aspect ratios. The authors propose a refined approach, termed Guided Anchoring, which offers improvements in efficiency and effectiveness.

Core Concepts

Guided Anchoring leverages semantic features to inform the placement of anchors, addressing critical inefficiencies in the traditional anchoring process. This method enables the prediction of probable locations for object centers along with optimal scales and aspect ratios at different positions. It achieves these through a Guided Anchoring Region Proposal Network (GA-RPN), which includes a feature adaptation module to address feature inconsistency issues. Additionally, the paper explores the integration of high-quality proposals to enhance detection performance.

Methodology

The proposed scheme revolves around the following innovations:

1. Anchor Location Prediction: The introduction of a probabilistic approach to determine where anchors should be placed, focusing on sub-regions of an image likely to contain objects.
2. Anchor Shape Prediction: Unlike conventional methods with predefined anchor shapes, this method predicts arbitrary anchor shapes guided by semantic content. This approach enhances recall by better accommodating objects with varying aspect ratios.
3. Feature Adaptation Module: A mechanism designed to ensure consistency between the features and the predicted anchor shapes, accomplished using deformable convolutional layers.

The approach employs these elements to generate anchors on multiple feature maps, akin to the FPN architecture, but with a shared parameter space across levels for efficiency.

Results and Contributions

The paper demonstrates that Guided Anchoring yields a 9.1% improvement in recall on the MS COCO dataset compared to the RPN baseline, whilst significantly reducing the number of anchors by 90%. Furthermore, when incorporated into popular detection systems like Fast R-CNN, Faster R-CNN, and RetinaNet, the method reports improvements in detection mean Average Precision (mAP) by 2.2%, 2.7%, and 1.2%, respectively.

The paper's contributions can be summarised as follows:

• A new, efficient anchoring scheme that predicts non-uniform and arbitrary shaped anchors rather than relying on dense, predefined ones.
• The formulation of a joint anchor distribution employing factorized conditional distributions tailored to specific locations.
• Highlighting the significance of feature and anchor alignment, offering a feature adaptation module as a solution.
• The introduction of a fine-tuning schedule leveraging GA-RPN proposals to enhance the performance of trained object detection models.

Implications and Future Directions

The implications of this paper are twofold. Practically, the proposed method can be directly applied to state-of-the-art object detection frameworks to improve both efficacy and computational efficiency. Theoretically, it provides insights into adaptive anchoring strategies, potently impacting research directions in object detection and proposal methodologies.

Looking ahead, this approach opens avenues for further research into even more dynamic anchoring systems, potentially driven by real-time learning from continuously incoming data or through reinforcement learning paradigms. Additionally, as computational power and detector complexity grow, integrating more sophisticated features in the proposal phase might offer continually improving performance metrics.

As the demand for high-precision and efficient object detection grows across various applications, methodologies such as Guided Anchoring will likely lead to significant advancements in both real-world tasks and theoretical research within the AI community.