WiMi Announced Asymmetric Spectral Network Algorithm
December 14 2023 - 8:00AM
WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"),
a leading global Hologram Augmented Reality ("AR") Technology
provider, today announced that its R&D team proposed an
asymmetric spectral network algorithm. The algorithm employs
asymmetric coordinate spectral spatial feature fusion to provide a
novel, end-to-end feature learning method for hyperspectral image
classification tasks. The algorithm's adaptive feature fusion
method is capable of extracting discriminative spectral spatial
features, and unlike common feature fusion methods, the algorithm
is more adaptable to multi-hop connectivity tasks while eliminating
the need for manual parameterization.
WiMi's asymmetric spectral network algorithm
solves the spectral noise problem through adaptive feature fusion.
The algorithm allows the network to adaptively fuse multiple pieces
of information to extract discriminative spectral-spatial features.
Unlike traditional feature fusion, this algorithm does not require
manual parameterization and is adapted to multi-hop connectivity
tasks. This adaptivity helps to efficiently handle complex spectral
data and improves the algorithm's ability to recognize real
signals.
In terms of the band correlation problem, the
asymmetric spectral network algorithm introduces a coordinate and
strip pooling module. Coordinates are used to obtain accurate
coordinate and channel information, which helps the network to
better understand the spatial structure of the data. Meanwhile, the
strip pooling module is used to avoid introducing irrelevant
information. The combination of these two techniques makes the
network more adaptive and better able to handle the complex band
correlations present in hyperspectral images.
WiMi's asymmetric spectral network algorithm
focuses on simplicity, which is to reduce the model complexity with
less training time. The algorithm successfully reduces the
complexity of the algorithm through an asymmetric learning model
and adaptive feature fusion while maintaining high classification
performance. This makes the algorithm more suitable for practical
application scenarios and provides higher efficiency for
hyperspectral image classification tasks.
WiMi's asymmetric spectral network algorithm
focuses not only on static scenes but also on dynamic scenes. Its
end-to-end feature learning approach and adaptive feature fusion
method enable the algorithm to better adapt to the ever-changing
information in hyperspectral images, thus improving the
classification accuracy in dynamic scenes. It effectively overcomes
the technical challenges in hyperspectral image classification and
brings a more efficient and accurate solution.
In addition, it introduces the key technology of
asymmetric coordinate spectral spatial feature fusion. The
algorithm learns the feature representation of hyperspectral images
end-to-end through an asymmetric learning model. Compared to
traditional methods, this asymmetric learning approach better
captures the complex relationships between pixels, enabling the
model to more accurately understand the non-uniformity of the
spatial distribution, thus improving the classification
accuracy.
The successful development of WiMi's asymmetric
spectral network algorithm provides greater feasibility for
real-world application scenarios. By reducing model complexity and
improving training and inference efficiency, the algorithm can be
better adapted to real-world requirements, especially in
decision-making and monitoring scenarios that require fast
response, demonstrating significant advantages. The introduction of
the algorithm will drive hyperspectral image classification
technology into a new stage of development. This is expected to
stimulate more research and innovation and drive the whole field
forward.
WiMi's asymmetric spectral network algorithm
provides a more accurate and efficient solution for hyperspectral
data analysis and processing in the fields of crop detection and
geological exploration. In the future, with the further
optimization of the algorithm, it will be applied to a wider range
of fields, such as environmental monitoring, weather prediction,
etc., providing more powerful support for various industries.
asymmetric spectral network algorithm will accelerate the deep
integration of scientific research and industry.
Considering the prevalence of dynamic scenes in
hyperspectral image classification tasks, WiMi will continue to
optimize the adaptability of the asymmetric spectral network
algorithm. By further improving the end-to-end learning approach
and adaptive feature fusion method, the algorithm is better adapted
to rapidly changing environments and improves classification
accuracy in dynamic scenes. WiMi's asymmetric spectral network
algorithm opens up new horizons in the field of hyperspectral image
classification, and will continue to play an important role in
scientific research, industrial applications, and technological
innovation.
About WIMI Hologram CloudWIMI Hologram Cloud,
Inc. (NASDAQ: WIMI) is a holographic cloud comprehensive technical
solution provider that focuses on professional areas including
holographic AR automotive HUD software, 3D holographic pulse LiDAR,
head-mounted light field holographic equipment, holographic
semiconductor, holographic cloud software, holographic car
navigation, and others. Its services and holographic AR
technologies include holographic AR automotive application, 3D
holographic pulse LiDAR technology, holographic vision
semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication, and other holographic AR technologies.
Safe Harbor StatementsThis press release
contains "forward-looking statements" within the Private Securities
Litigation Reform Act of 1995. These forward-looking statements can
be identified by terminology such as "will," "expects,"
"anticipates," "future," "intends," "plans," "believes,"
"estimates," and similar statements. Statements that are not
historical facts, including statements about the Company's beliefs
and expectations, are forward-looking statements. Among other
things, the business outlook and quotations from management in this
press release and the Company's strategic and operational plans
contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other
risks is included in the Company's annual report on Form 20-F and
the current report on Form 6-K and other documents filed with the
SEC. All information provided in this press release is as of the
date of this press release. The Company does not undertake any
obligation to update any forward-looking statement except as
required under applicable laws.
ContactsWIMI Hologram Cloud Inc.Email:
pr@wimiar.comTEL: 010-53384913
ICR, LLCRobin YangTel: +1 (646) 975-9495Email:
wimi@icrinc.com
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