June 11, 2024, 4:51 a.m. | Israt Zarin Era, Imtiaz Ahmed, Zhichao Liu, Srinjoy Das

cs.CV updates on arXiv.org arxiv.org

arXiv:2312.04063v2 Announce Type: replace
Abstract: Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the manufacturing domain, accurate image-based defect segmentation is imperative to ensure product quality and facilitate real-time process control. However, such tasks are often characterized by multiple challenges including the absence of labels and the requirement for low latency inference among others. To address these issues, …

abstract additive manufacturing arxiv astronomy biology computer computer vision cs.cv domain driving fields foundation generated image manufacturing paradigm performance prompts replace robotics segment segment anything segmentation shift tasks type unsupervised vision

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