Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
<|reference_start|>Understanding Multi-Granularity for Open-Vocabulary Part Segmentation: Open-vocabulary part segmentation (OVPS) is an emerging research area focused on segmenting fine-grained entities based on diverse and previously unseen vocabularies. Our study highlights the inherent complexities of part segmentation due to intricate boundaries and diverse granularity, reflecting the knowledge-based nature of part identification. To address these challenges, we propose PartCLIPSeg, a novel framework utilizing generalized parts and object-level contexts to mitigate the lack of generalization in fine-grained parts. PartCLIPSeg integrates competitive part relationships and attention control techniques, alleviating ambiguous boundaries and underrepresented parts. Experimental results demonstrate that PartCLIPSeg outperforms existing state-of-the-art OVPS methods, offering refined segmentation and an advanced understanding of part relationships in images. Through extensive experiments, our model demonstrated an improvement over the state-of-the-art models on the Pascal-Part-116, ADE20K-Part-234, and PartImageNet datasets.<|reference_end|>
@article{choi2024understanding,
    title={Understanding Multi-Granularity for Open-Vocabulary Part Segmentation},
    author={Jiho Choi, Seonho Lee, Seungho Lee, Minhyun Lee, Hyunjung Shim},
    journal={arXiv preprint arXiv:2406.11384},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11384},
    primaryClass={cs.CV}
}