155,591 research outputs found
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple stages and components in the pipelines. In this work, we propose a
simple yet powerful pipeline that yields fast and accurate text detection in
natural scenes. The pipeline directly predicts words or text lines of arbitrary
orientations and quadrilateral shapes in full images, eliminating unnecessary
intermediate steps (e.g., candidate aggregation and word partitioning), with a
single neural network. The simplicity of our pipeline allows concentrating
efforts on designing loss functions and neural network architecture.
Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500
demonstrate that the proposed algorithm significantly outperforms
state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR
2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps
at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral
image registration and fusion. Our method for fusion combines images from
different spectral channels into a single fused image by different approaches
for low and high frequency signals. A prerequisite of fusion is a stage of
geometric alignment between the spectral bands, commonly referred to as
registration. Unfortunately, common methods for image registration of a single
spectral channel do not yield reasonable results on images from different
modalities. For that end, we introduce a new algorithm for multi-spectral image
registration, based on a novel edge descriptor of feature points. Our method
achieves an accurate alignment of a level that allows us to further fuse the
images. As our experiments show, we produce a high quality of multi-spectral
image registration and fusion under many challenging scenarios
A Fast and Efficient Incremental Approach toward Dynamic Community Detection
Community detection is a discovery tool used by network scientists to analyze
the structure of real-world networks. It seeks to identify natural divisions
that may exist in the input networks that partition the vertices into coherent
modules (or communities). While this problem space is rich with efficient
algorithms and software, most of this literature caters to the static use-case
where the underlying network does not change. However, many emerging real-world
use-cases give rise to a need to incorporate dynamic graphs as inputs.
In this paper, we present a fast and efficient incremental approach toward
dynamic community detection. The key contribution is a generic technique called
, which examines the most recent batch of changes made to an
input graph and selects a subset of vertices to reevaluate for potential
community (re)assignment. This technique can be incorporated into any of the
community detection methods that use modularity as its objective function for
clustering. For demonstration purposes, we incorporated the technique into two
well-known community detection tools. Our experiments demonstrate that our new
incremental approach is able to generate performance speedups without
compromising on the output quality (despite its heuristic nature). For
instance, on a real-world network with 63M temporal edges (over 12 time steps),
our approach was able to complete in 1056 seconds, yielding a 3x speedup over a
baseline implementation. In addition to demonstrating the performance benefits,
we also show how to use our approach to delineate appropriate intervals of
temporal resolutions at which to analyze an input network
Considerations about multistep community detection
The problem and implications of community detection in networks have raised a
huge attention, for its important applications in both natural and social
sciences. A number of algorithms has been developed to solve this problem,
addressing either speed optimization or the quality of the partitions
calculated. In this paper we propose a multi-step procedure bridging the
fastest, but less accurate algorithms (coarse clustering), with the slowest,
most effective ones (refinement). By adopting heuristic ranking of the nodes,
and classifying a fraction of them as `critical', a refinement step can be
restricted to this subset of the network, thus saving computational time.
Preliminary numerical results are discussed, showing improvement of the final
partition.Comment: 12 page
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