Attribute Conjunction Learning with Recurrent Neural Network.

Searching images with multi-attribute queries shows practical significance in various real world applications. The key problem in this task is how to effectively and efficiently learn from the conjunction of query attributes. In this paper, we propose Attribute Conjunction Recurrent Neural Network AC-RNN to tackle this problem. Attributes involved in a query are mapped into the hidden units and combined in a recurrent way to generate the representation of the attribute conjunction, which is then used to compute a multi-attribute classifier as the output. To mitigate the data imbalance problem of multi-attribute queries, we propose a data weighting procedure in attribute conjunction learning with small positive samples. We also discuss on the influence of attribute order in a query and present two methods based on attention mechanism and ensemble learning respectively to further boost the performance. Experimental results on aPASCAL, ImageNet Attributes and LFWA datasets show that our method consistently and significantly outperforms the other comparison methods on all types of queries. The software related to this paper is available at https://github.com/GriffinLiang/AC-RNN.