  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.
