From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
This work addresses a gap in multi-attribute learning for applications like image annotation by incorporating personal diversity and attribute correlations, but it appears incremental as it builds on existing methods without claiming broad SOTA.
The paper tackles the problem of learning visual attributes by jointly modeling user-specific labels and global consensus across multiple attributes, proposing a model that integrates common cognition, attribute-specific bias, and user-specific bias with Lasso penalties, and demonstrates its effectiveness empirically.
Visual attributes, which refer to human-labeled semantic annotations, have gained increasing popularity in a wide range of real world applications. Generally, the existing attribute learning methods fall into two categories: one focuses on learning user-specific labels separately for different attributes, while the other one focuses on learning crowd-sourced global labels jointly for multiple attributes. However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes. To overcome this challenge, we propose a novel model to learn user-specific predictors across multiple attributes. In our proposed model, the diversity of personalized opinions and the intrinsic relationship among multiple attributes are unified in a common-to-special manner. To this end, we adopt a three-component decomposition. Specifically, our model integrates a common cognition factor, an attribute-specific bias factor and a user-specific bias factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage efficient feature selection. Furthermore, theoretical analysis is conducted to show that our proposed method could reach reasonable performance. Eventually, the empirical study carried out in this paper demonstrates the effectiveness of our proposed method.