In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations. Further, we make this architecture suitable for mobile-device application by adopting the bilinear CNN to make the multi-attribute recognition model smaller (in terms of the number of parameters). The experiments run on the Dress category of DeepFashion In-Shop Clothes Retrieval and CUB200 datasets show that the results of instance retrieval at fine-grained level are promising for these datasets, specially in terms of texture and color.
@article{arxiv.1811.02949,
title = {Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition},
author = {Roshanak Zakizadeh and Yu Qian and Michele Sasdelli and Eduard Vazquez},
journal= {arXiv preprint arXiv:1811.02949},
year = {2018}
}