Weilin Huang

Title: Weilin Huang: Innovator in Action Recognition and Image Classification

Introduction

Weilin Huang is a prominent inventor based in Shenzhen, China. He has made significant contributions to the fields of action recognition and multi-label image classification. With a total of 7 patents, his work focuses on developing advanced technologies that enhance loss prevention in retail and improve image classification systems.

Latest Patents

One of his latest patents is titled "Retail inventory shrinkage reduction via action recognition." This disclosure includes technologies for action recognition in general. The disclosed system may automatically detect various types of actions in a video, including reportable actions that cause shrinkage in a practical application for loss prevention in the retail industry. Further, appropriate responses may be invoked if a reportable action is recognized. In some embodiments, a three-branch architecture may be used in a machine learning model for action and/or activity recognition. The three-branch architecture may include a main branch for action recognition, an auxiliary branch for learning/identifying an actor (e.g., human parsing) related to an action, and an auxiliary branch for learning/identifying a scene related to an action. In this three-branch architecture, the knowledge of the actor and the scene may be integrated in two different levels for action and/or activity recognition.

Another notable patent is "Decoupling category-wise independence and relevance with self-attention for multi-label image classification." Methods and systems are provided for generating a multi-label classification system. The multi-label classification system can use a multi-label classification neural network system to identify one or more labels for an image. The multi-label classification system can explicitly take into account the relationship between classes in identifying labels. A relevance sub-network of the multi-label classification neural network system can capture relevance information between the classes. Such a relevance sub-network can decouple independence between classes to focus learning on relevance between the classes.

Career Highlights

Weilin Huang has worked with Shenzhen Malong Technologies Co., Ltd., where he has contributed to various innovative projects. His expertise in machine learning and computer vision has positioned him as a key player in the development of cutting-edge technologies.

Collaborations

Some of his notable coworkers include Matthew Robert Scott and Sheng Guo. Their collaboration has likely fostered an environment of innovation and creativity, leading to the development of impactful technologies.

Conclusion

Weilin Huang's contributions