The diffusion from stivensss

diffusion's Introduction

This is a faster and improved version of diffusion retrieval, inspired by diffusion-retrieval.

Reference:

F. Yang, R. Hinami, Y. Matsui, S. Ly, S. Satoh, "Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing", AAAI 2019. [arXiv]

If you would like to understand further details of our method, these slides may provide some help.

Features

All random walk processes are moved to offline, making the online search remarkably fast
In contrast to previous works, we achieved better performance by applying late truncation instead of early truncation to the graph

Requirements

Install Facebook FAISS by running conda install faiss-cpu -c pytorch

Optional: install the faiss-gpu under the instruction according to your CUDA version

Install joblib by running conda install joblib
Install tqdm by running conda install tqdm

Parameters

All parameters can be modified in Makefile. You may want to edit DATASET and FEATURE_TYPE to test all combinations of each dataset and each feature type. Another parameter truncation_size is set to 1000 by default, for large datasets like Oxford105k and Paris106k, changing it to 5000 will improve the performance.

Run

Run make download to download files needed in experiments;
Run make mat2npy to convert .mat files to .npy files;
Run make rank to get the results. If you have GPUs, try using commands like CUDA_VISIBLE_DEVICES=0,1 make rank, 0,1 are examples of GPU ids.

Note: on Oxford5k and Paris6k datasets, the truncation_size parameter should be no larger than 1024 when using GPUs according to FAISS's limitation. You can use CPUs instead.