Deep hashing with multi-task learning for large-scale instance-level vehicle search
Hashing is a hot research topic in large-scale image search, due to its low memory cost and fast search speed. Recently, deep hashing, which adapts deep convolutional neural networks into hashing, has attracted much attention. In this paper, we propose a new supervised deep hashing method to deal with large-scale instance-level vehicle search, and make the following contributions. Firstly, multi-task learning is employed to learn the hash code, which exploits the available multiple labels of each vehicle, i.e., ID, model, and color. Secondly, differing from several deep hashing methods, which utilize sigmoid or tanh as the activation function of the hash layer, rectified linear unit is adopted in this paper and shows better performance. Thirdly, taking GoogLeNet as the base network, we show that search performance can be promoted significantly, by learning the network's parameters from scratch on our vehicle data. Finally, we perform extensive experiments on a large-scale dataset with up to one million vehicles. The experimental results demonstrate the effectiveness of the proposed method, which outperforms single task deep hashing methods with classification and triplet ranking losses, respectively.