SpotCrack: Leveraging a Lightweight Framework for Crack Segmentation in Infrastructure
Document Type
Conference Proceeding
Publication Title
Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Abstract
In today's data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.
DOI
10.1109/ICCE59016.2024.10444350
Publication Date
1-1-2024
Keywords
Face Recognition, Google Scholar, Information Retrieval, Language Model
Recommended Citation
W. Kang et al., "SpotCrack: Leveraging a Lightweight Framework for Crack Segmentation in Infrastructure," Digest of Technical Papers - IEEE International Conference on Consumer Electronics, Jan 2024.
The definitive version is available at https://doi.org/10.1109/ICCE59016.2024.10444350