7,389 research outputs found
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Visual place recognition is challenging in the urban environment and is
usually viewed as a large scale image retrieval task. The intrinsic challenges
in place recognition exist that the confusing objects such as cars and trees
frequently occur in the complex urban scene, and buildings with repetitive
structures may cause over-counting and the burstiness problem degrading the
image representations. To address these problems, we present an Attention-based
Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner
for place recognition. One main component of APANet, the spatial pyramid
pooling, can effectively encode the multi-size buildings containing
geo-information. The other one, the attention block, is adopted as a region
evaluator for suppressing the confusing regional features while highlighting
the discriminative ones. When testing, we further propose a simple yet
effective PCA power whitening strategy, which significantly improves the widely
used PCA whitening by reasonably limiting the impact of over-counting.
Experimental evaluations demonstrate that the proposed APANet outperforms the
state-of-the-art methods on two place recognition benchmarks, and generalizes
well on standard image retrieval datasets.Comment: Accepted to ACM Multimedia 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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