Region Proposal by Guided Anchoring

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.

ROIMIX: Proposal-Fusion Among Multiple Images for Underwater Object Detection

“…Therefore, Lin et al [17] proposes Feature Pyramid Networks (FPN), which combines hierarchical features to make better predictions. In recent years, there have been other variants of Faster R-CNN [2,27,28,29,30]. Our method is potentially versatile and can be applied to two-stage detectors.…”

STELA: A Real-Time Scene Text Detector With Learned Anchor

“…Especially, the regression targets of learned anchor is not arbitrary. As refer in [35], one of general rules for a reasonable anchor design is alignment. To use convolutional features as anchor representations, the center of an anchor need to be well aligned with feature map pixels.…”

“…This changing will bring twofold benefit: reducing manual attention on anchors and improving efficiency at inference stage. First, the shapes and scales of anchors has to be predefined for different tasks, and this must be careful because a wrong design may harm the performance of detection [35]. Second, most anchors correspond to false candidates which are irrelevant to the targets, and meanwhile a large number of anchors can lead to significant computational cost when the network involves heavy heads.…”

AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

“…While almost all the state of the art object detectors employ pre-defined anchors, anchor-free object detectors [9], [17], [18], [39], [40] have received much attention in recent years because of their better adaptability towards different datasets. Representative approaches include CornerNet [18] and Cen-terNet [9].…”

“…Motivated by these observations, the "Anchor-Free" approaches [9], [18], [40], [42], [44] have received much attention recently, where the anchor mechanism is removed and objects are represented as keypoints. For instance, CornerNet [18] represents an object as a pair of keypoints (top-left and bottom-right corners).…”