Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives
(attributes and objects) from seen compositions and recognize unseen
attribute-object compositions. Existing CZSL datasets focus on single
attributes, neglecting the fact that objects naturally exhibit multiple
interrelated attributes. Real-world objects often possess multiple interrelated
attributes, and current datasets' narrow attribute scope and single attribute
labeling introduce annotation biases, undermining model performance and
evaluation. To address these limitations, we introduce the Multi-Attribute
Composition (MAC) dataset, encompassing 18,217 images and 11,067 compositions
with comprehensive, representative, and diverse attribute annotations. MAC
includes an average of 30.2 attributes per object and 65.4 objects per
attribute, facilitating better multi-attribute composition predictions. Our
dataset supports deeper semantic understanding and higher-order attribute
associations, providing a more realistic and challenging benchmark for the CZSL
task. We also develop solutions for multi-attribute compositional learning and
propose the MM-encoder to disentangling the attributes and objects.

通过开发多属性组合数据集（MAC），提出了多属性组合学习的解决方案，并提出了 MM 编码器来解析属性和对象的关联，以支持上下文无关学习（CZSL）任务。

MAC：多属性组合零样本学习的基准

MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot  Learning

Point clouds have attracted increasing attention. Significant progress has
been made in methods for point cloud analysis, which often requires costly
human annotation as supervision. To address this issue, we propose a novel
self-contrastive learning for self-supervised point cloud representation
learning, aiming to capture both local geometric patterns and nonlocal semantic
primitives based on the nonlocal self-similarity of point clouds. The
contributions are two-fold: on the one hand, instead of contrasting among
different point clouds as commonly employed in contrastive learning, we exploit
self-similar point cloud patches within a single point cloud as positive
samples and otherwise negative ones to facilitate the task of contrastive
learning. On the other hand, we actively learn hard negative samples that are
close to positive samples for discriminative feature learning. Experimental
results show that the proposed method achieves state-of-the-art performance on
widely used benchmark datasets for self-supervised point cloud segmentation and
transfer learning for classification.

本研究提出了自对比学习方法用于自我监督点云表示学习，以捕捉点云的局部几何模式和非局部语义基元。实验结果表明，该方法在自我监督点云分割和分类转移学习等领域的基准数据集上取得了最先进的性能。