Attribute-Aware Attention Model for Fine-grained Representation Learning
This work addresses fine-grained representation learning for applications such as person re-identification and image retrieval, offering a novel attention-based method that is incremental in improving local and global feature integration.
The paper tackles the problem of learning discriminative fine-grained representations in computer vision by proposing an Attribute-Aware Attention Model (A^3M) that simultaneously learns local attribute and global category features through a reciprocal attention process, achieving effectiveness as demonstrated in experiments on datasets like Market-1501 and CUB-200-2011.
How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model contains two attention models: attribute-guided attention module uses attribute information to help select category features in different regions, at the same time, category-guided attention module selects local features of different attributes with the help of category cues. Through this attribute-category reciprocal process, local and global features benefit from each other. Finally, the resulting feature contains more intrinsic information for image recognition instead of the noisy and irrelevant features. Extensive experiments conducted on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the effectiveness of our $A^3M$. Code is available at https://github.com/iamhankai/attribute-aware-attention.