6,550 research outputs found
Adaptive Object Detection Using Adjacency and Zoom Prediction
State-of-the-art object detection systems rely on an accurate set of region
proposals. Several recent methods use a neural network architecture to
hypothesize promising object locations. While these approaches are
computationally efficient, they rely on fixed image regions as anchors for
predictions. In this paper we propose to use a search strategy that adaptively
directs computational resources to sub-regions likely to contain objects.
Compared to methods based on fixed anchor locations, our approach naturally
adapts to cases where object instances are sparse and small. Our approach is
comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach
while using two orders of magnitude fewer anchors on average. Code is publicly
available.Comment: Accepted to CVPR 201
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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