3,511 research outputs found
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Rotation-invariant face detection, i.e. detecting faces with arbitrary
rotation-in-plane (RIP) angles, is widely required in unconstrained
applications but still remains as a challenging task, due to the large
variations of face appearances. Most existing methods compromise with speed or
accuracy to handle the large RIP variations. To address this problem more
efficiently, we propose Progressive Calibration Networks (PCN) to perform
rotation-invariant face detection in a coarse-to-fine manner. PCN consists of
three stages, each of which not only distinguishes the faces from non-faces,
but also calibrates the RIP orientation of each face candidate to upright
progressively. By dividing the calibration process into several progressive
steps and only predicting coarse orientations in early stages, PCN can achieve
precise and fast calibration. By performing binary classification of face vs.
non-face with gradually decreasing RIP ranges, PCN can accurately detect faces
with full RIP angles. Such designs lead to a real-time
rotation-invariant face detector. The experiments on multi-oriented FDDB and a
challenging subset of WIDER FACE containing rotated faces in the wild show that
our PCN achieves quite promising performance.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018). Code: \url{https://github.com/Jack-CV/PCN
Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
Lesions characterized by computed tomography (CT) scans, are arguably often
elliptical objects. However, current lesion detection systems are predominantly
adopted from the popular Region Proposal Networks (RPNs) that only propose
bounding boxes without fully leveraging the elliptical geometry of lesions. In
this paper, we present Gaussian Proposal Networks (GPNs), a novel extension to
RPNs, to detect lesion bounding ellipses. Instead of directly regressing the
rotation angle of the ellipse as the common practice, GPN represents bounding
ellipses as 2D Gaussian distributions on the image plain and minimizes the
Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground
truth Gaussian for object localization. We show the KL divergence loss
approximately incarnates the regression loss in the RPN framework when the
rotation angle is 0. Experiments on the DeepLesion dataset show that GPN
significantly outperforms RPN for lesion bounding ellipse detection thanks to
lower localization error. GPN is open sourced at
https://github.com/baidu-research/GP
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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