Attribute-Aware Feature Encoding for Object Recognition and Segmentation

Existing multi-task models for object recognition and segmentation have verified the effectiveness of joint optimization of two semantic tasks. However, learning discriminative representations with insufficient training data and redundant contextual information from the background remains challenging. Semantic attributes are designed as powerful and informative mid-level features that 1) share information across categories to model the interclass correlation and that 2) can be localized in the object region to benefit foreground extraction. This paper introduces a novel attribute-aware feature encoding (AFE) module to a multi-task network for object recognition and segmentation with the aim of improving both semantic tasks by regularizing feature encoding with auxiliary attribute learning. Intuitively, attribute learning in our method not only provides extra supervision signals to capture interclass correlation in object classification but also refines the output of object segmentation via weakly supervised attribute localization. The experimental results on two public benchmarks show that our method yields remarkable improvement in both semantic tasks and auxiliary attribute estimation over existing methods.