guided_anchoring

Guided Anchoring

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%.

Results and Models

The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).

Method	Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	AR 1000	Config	Download
GA-RPN	R-50-FPN	caffe	1x	5.3	15.8	68.4	config	model | log
GA-RPN	R-101-FPN	caffe	1x	7.3	13.0	69.5	config	model | log
GA-RPN	X-101-32x4d-FPN	pytorch	1x	8.5	10.0	70.6	config	model | log
GA-RPN	X-101-64x4d-FPN	pytorch	1x	7.1	7.5	71.2	config	model | log

Method	Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	Config	Download
GA-Faster RCNN	R-50-FPN	caffe	1x	5.5		39.6	config	model | log
GA-Faster RCNN	R-101-FPN	caffe	1x	7.5		41.5	config	model | log
GA-Faster RCNN	X-101-32x4d-FPN	pytorch	1x	8.7	9.7	43.0	config	model | log
GA-Faster RCNN	X-101-64x4d-FPN	pytorch	1x	11.8	7.3	43.9	config	model | log
GA-RetinaNet	R-50-FPN	caffe	1x	3.5	16.8	36.9	config	model | log
GA-RetinaNet	R-101-FPN	caffe	1x	5.5	12.9	39.0	config	model | log
GA-RetinaNet	X-101-32x4d-FPN	pytorch	1x	6.9	10.6	40.5	config	model | log
GA-RetinaNet	X-101-64x4d-FPN	pytorch	1x	9.9	7.7	41.3	config	model | log

• In the Guided Anchoring paper, score_thr is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.

• Performance on COCO test-dev benchmark are shown as follows.

Method	Backbone	Style	Lr schd	Aug Train	Score thr	AP	AP_50	AP_75	AP_small	AP_medium	AP_large	Download
GA-Faster RCNN	R-101-FPN	caffe	1x	F	0.05
GA-Faster RCNN	R-101-FPN	caffe	1x	F	0.001
GA-RetinaNet	R-101-FPN	caffe	1x	F	0.05
GA-RetinaNet	R-101-FPN	caffe	2x	T	0.05

Citation

We provide config files to reproduce the results in the CVPR 2019 paper for Region Proposal by Guided Anchoring.

@inproceedings{wang2019region,
    title={Region Proposal by Guided Anchoring},
    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    year={2019}
}