346 research outputs found
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Thanks to the efficient retrieval speed and low storage consumption, learning
to hash has been widely used in visual retrieval tasks. However, existing
hashing methods assume that the query and retrieval samples lie in homogeneous
feature space within the same domain. As a result, they cannot be directly
applied to heterogeneous cross-domain retrieval. In this paper, we propose a
Generalized Image Transfer Retrieval (GITR) problem, which encounters two
crucial bottlenecks: 1) the query and retrieval samples may come from different
domains, leading to an inevitable {domain distribution gap}; 2) the features of
the two domains may be heterogeneous or misaligned, bringing up an additional
{feature gap}. To address the GITR problem, we propose an Asymmetric Transfer
Hashing (ATH) framework with its unsupervised/semi-supervised/supervised
realizations. Specifically, ATH characterizes the domain distribution gap by
the discrepancy between two asymmetric hash functions, and minimizes the
feature gap with the help of a novel adaptive bipartite graph constructed on
cross-domain data. By jointly optimizing asymmetric hash functions and the
bipartite graph, not only can knowledge transfer be achieved but information
loss caused by feature alignment can also be avoided. Meanwhile, to alleviate
negative transfer, the intrinsic geometrical structure of single-domain data is
preserved by involving a domain affinity graph. Extensive experiments on both
single-domain and cross-domain benchmarks under different GITR subtasks
indicate the superiority of our ATH method in comparison with the
state-of-the-art hashing methods
Pairwise Teacher-Student Network for Semi-Supervised Hashing
Hashing method maps similar high-dimensional data to binary hashcodes with
smaller hamming distance, and it has received broad attention due to its low
storage cost and fast retrieval speed. Pairwise similarity is easily obtained
and widely used for retrieval, and most supervised hashing algorithms are
carefully designed for the pairwise supervisions. As labeling all data pairs is
difficult, semi-supervised hashing is proposed which aims at learning efficient
codes with limited labeled pairs and abundant unlabeled ones. Existing methods
build graphs to capture the structure of dataset, but they are not working well
for complex data as the graph is built based on the data representations and
determining the representations of complex data is difficult. In this paper, we
propose a novel teacher-student semi-supervised hashing framework in which the
student is trained with the pairwise information produced by the teacher
network. The network follows the smoothness assumption, which achieves
consistent distances for similar data pairs so that the retrieval results are
similar for neighborhood queries. Experiments on large-scale datasets show that
the proposed method reaches impressive gain over the supervised baselines and
is superior to state-of-the-art semi-supervised hashing methods
Efficient end-to-end learning for quantizable representations
Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio
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