MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation
<|reference_start|>MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation: Instance segmentation, a cornerstone task in computer vision, has wide-ranging applications in diverse industries. The advent of deep learning and artificial intelligence has underscored the criticality of training effective models, particularly in data-scarce scenarios - a concern that resonates in both academic and industrial circles. A significant impediment in this domain is the resource-intensive nature of procuring high-quality, annotated data for instance segmentation, a hurdle that amplifies the challenge of developing robust models under resource constraints. In this context, the strategic integration of a visual prior into the training dataset emerges as a potential solution to enhance congruity with the testing data distribution, consequently reducing the dependency on computational resources and the need for highly complex models. However, effectively embedding a visual prior into the learning process remains a complex endeavor. Addressing this challenge, we introduce the MISS (Memory-efficient Instance Segmentation System) framework. MISS leverages visual inductive prior flow propagation, integrating intrinsic prior knowledge from the Synergy-basketball dataset at various stages: data preprocessing, augmentation, training, and inference. Our empirical evaluations underscore the efficacy of MISS, demonstrating commendable performance in scenarios characterized by limited data availability and memory constraints.<|reference_end|>
@article{hsu2024miss:,
    title={MISS: Memory-efficient Instance Segmentation Framework By Visual
  Inductive Priors Flow Propagation},
    author={Chih-Chung Hsu and Chia-Ming Lee},
    journal={arXiv preprint arXiv:2403.11576},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11576},
    primaryClass={cs.CV cs.MM}
}