  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.
