Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding
<|reference_start|>Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding: One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr's Primal Sketch combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis. Given the progress made in multiple areas of AI since then, we have developed a new framework inspired by Marr's work, the Hybrid Primal Sketch, which combines computer vision components into an ensemble to produce sketch-like entities which are then further processed by CogSketch, our model of high-level human vision, to produce both more detailed shape representations and scene representations which can be used for data-efficient learning via analogical generalization. This paper describes our theoretical framework, summarizes several previous experiments, and outlines a new experiment in progress on diagram understanding.<|reference_end|>
@article{forbus2024hybrid,
    title={Hybrid Primal Sketch: Combining Analogy, Qualitative Representations,
  and Computer Vision for Scene Understanding},
    author={Kenneth D. Forbus, Kezhen Chen, Wangcheng Xu, Madeline Usher},
    journal={arXiv preprint arXiv:2407.04859},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04859},
    primaryClass={cs.CV cs.AI}
}