Jen-Wei Wang
Papers
2
Total Citations
16
H-Index
2
About
Jen-Wei Wang is a researcher working at the intersection of computer vision, natural language processing, and robotics, with a particular focus on visual grounding and embodied AI. His most notable contribution is the development of OCID-Ref, a pioneering 3D robotic dataset that pairs embodied language with cluttered scene environments, enabling machines to better identify and interact with occluded objects in real-world settings such as offices and warehouses. This work addresses a critical gap in visual grounding research, where prior datasets failed to capture the complexity of practical robotic working environments. By bridging language understanding with 3D spatial reasoning, Wang's research advances the ability of robots to interpret natural language references and locate objects even under challenging occlusion conditions. Published at the prestigious NAACL 2021 conference, OCID-Ref has garnered meaningful attention within the research community, accumulating citations that reflect its utility as a benchmark resource. Wang's work represents an important step toward developing robots that can collaborate naturally and effectively with humans in unstructured, real-world environments, making it highly relevant for researchers in human-robot interaction, grounded language learning, and embodied intelligence.
Research Focus
Key Achievements
Top Papers
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