This paper proposes the SVDNet for retrieval problems, with focus on the
application of person re-identification (re-ID). We view each weight vector
within a fully connected (FC) layer in a convolutional neuron network (CNN) as
a projection basis...
It is observed that the weight vectors are usually highly
correlated. This problem leads to correlations among entries of the FC
descriptor, and compromises the retrieval performance based on the Euclidean
distance. To address the problem, this paper proposes to optimize the deep
representation learning process with Singular Vector Decomposition (SVD). Specifically, with the restraint and relaxation iteration (RRI) training
scheme, we are able to iteratively integrate the orthogonality constraint in
CNN training, yielding the so-called SVDNet. We conduct experiments on the
Market-1501, CUHK03, and Duke datasets, and show that RRI effectively reduces
the correlation among the projection vectors, produces more discriminative FC
descriptors, and significantly improves the re-ID accuracy. On the Market-1501
dataset, for instance, rank-1 accuracy is improved from 55.3% to 80.5% for
CaffeNet, and from 73.8% to 82.3% for ResNet-50.