EMCNet : Graph-Nets for Electron Micrographs Classification
<|reference_start|>EMCNet : Graph-Nets for Electron Micrographs Classification: Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach.<|reference_end|>
@article{srinivas2024emcnet,
    title={EMCNet : Graph-Nets for Electron Micrographs Classification},
    author={Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana},
    journal={arXiv preprint arXiv:2409.03767},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03767},
    primaryClass={cs.CV cs.LG}
}