SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

arXiv:2404.06638v1 Announce Type: cross
Abstract: Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries located? what segments are logically similar? - change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party …

abstract art arxiv autonomous autonomous driving boosting change cond-mat.mtrl-sci cs.cv diagnostics domain driving electron foundation generate however image medical sam scale segmentation semantic semantics state tasks type zero-shot