Attribute-Aware Attention Model for Fine-grained Representation Learning

Abstract

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 ($A^{3}M$), 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 $A^{3}M$. Code is available at https://github.com/iamhankai/attribute-aware-attention.

Accepted by ACM Multimedia 2018 (Oral). Code is available at https://github.com/iamhankai/attribute-aware-attention

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Improving Person Re-identification by Attribute and Identity Learning
• A Discriminatively Learned CNN Embedding for Person Re-identification
• Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
• Object-Part Attention Model for Fine-grained Image Classification
• Person Re-Identification by Camera Correlation Aware Feature Augmentation
• Deeply-Learned Part-Aligned Representations for Person Re-Identification
• Mining Discriminative Triplets of Patches for Fine-Grained Classification
• Hard-Aware Deeply Cascaded Embedding