A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects

Xiaoyang Wang, Qiang Ji; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2120-2127

This paper proposes a unified probabilistic model to model the relationships between attributes and objects for attribute prediction and object recognition. As a list of semantically meaningful properties of objects, attributes generally relate to each other statistically. In this paper, we propose a unified probabilistic model to automatically discover and capture both the object-dependent and objectindependent attribute relationships. The model utilizes the captured relationships to benefit both attribute prediction and object recognition. Experiments on four benchmark attribute datasets demonstrate the effectiveness of the proposed unified model for improving attribute prediction as well as object recognition in both standard and zero-shot learning cases.