WiMi Announced Asymmetric Spectral Network Algorithm

By: WiMi Hologram Cloud, Inc. via GlobeNewswire

December 14, 2023 at 08:00 AM EST

Beijing, Dec. 14, 2023 (GLOBE NEWSWIRE) -- 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 Cloud
WIMI 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 Statements
This 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.

Contacts
WIMI Hologram Cloud Inc.
Email: pr@wimiar.com
TEL: 010-53384913

ICR, LLC
Robin Yang
Tel: +1 (646) 975-9495
Email: wimi@icrinc.com

About Us

About Cabling Installation & Maintenance

Contact Cabling Installation & Maintenance

Editorial

Advertising and Sponsorship Sales

Subscriptions and Memberships

Feedback/Problems

WiMi Announced Asymmetric Spectral Network Algorithm

More News