ACT, Australia

Yiran Zhong

This inventor holds 1 USPTO granted patent. Top assignee: Inception Institute of Artificial Intelligence, Ltd.. Active years: 2024.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Yiran Zhong. Retrieved from https://idiyas.com/inventor/yiran-zhong-ac3hde0t

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Calculating rank

% Patents Active = 100.0

Average Co-Inventor Count = 7.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Yiran Zhong and Multimodal Saliency Detection.

Introduction

Yiran Zhong is an inventor associated with the ACT, Australia. His patent work addresses multimodal saliency detection, combining visual appearance and depth information to identify salient regions in images.

Latest Patents

Zhong is associated with 1 patent: “Complementary Learning for Multi-Modal Saliency Detection.” The invention models complementary information from image appearance, including color, and depth. It uses mutual-information minimization as a regularizer to reduce redundancy between RGB appearance features and geometric depth features in a latent space. The resulting features from both modalities are then fused for saliency detection.

Career Highlights

Zhong is associated with the Inception Institute of Artificial Intelligence, Ltd. His patent reflects work on integrating visual and geometric information to improve computational image analysis.

Collaborations

The supplied information lists Yuchao Dai and Deng-Ping Fan as Zhong’s coworkers. Their names are associated with his professional collaborations, though specific roles in the patent are not provided.

Conclusion

Yiran Zhong’s patent focuses on combining complementary appearance and depth features, offering a multimodal approach to saliency detection.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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