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.

利用广义部分和物体级上下文来缓解精细部分通用化不足，并整合竞争性部分关系和注意力控制技术，以改善图像中歧义边界和代表性不足的部分，PartCLIPSeg 在各种数据集上表现出色，提供了精细的分割和对部分关系的高级理解。

了解开放词汇部分分割的多粒度

Understanding Multi-Granularity for Open-Vocabulary Part Segmentation

This paper is on human pose estimation using Convolutional Neural Networks.
Our main contribution is a CNN cascaded architecture specifically designed for
learning part relationships and spatial context, and robustly inferring pose
even for the case of severe part occlusions. To this end, we propose a
detection-followed-by-regression CNN cascade. The first part of our cascade
outputs part detection heatmaps and the second part performs regression on
these heatmaps. The benefits of the proposed architecture are multi-fold: It
guides the network where to focus in the image and effectively encodes part
constraints and context. More importantly, it can effectively cope with
occlusions because part detection heatmaps for occluded parts provide low
confidence scores which subsequently guide the regression part of our network
to rely on contextual information in order to predict the location of these
parts. Additionally, we show that the proposed cascade is flexible enough to
readily allow the integration of various CNN architectures for both detection
and regression, including recent ones based on residual learning. Finally, we
illustrate that our cascade achieves top performance on the MPII and LSP data
sets. Code can be downloaded from this http URL

本研究利用卷积神经网络进行人体姿态估计，通过利用关系和空间上下文，提出了一种特殊的 CNN 级联架构，并能够在部分遮挡的情况下，鲁棒地推断姿势，该级联架构能够指导网络集中精力在图像的哪个位置，并明确编码部分限制和上下文约束，并能够应对遮挡。我们的级联结构表现出色，能够在 MPII 和 LSP 数据集上取得最佳的表现。