386 research outputs found
Unsupervised Semantic Hashing with Pairwise Reconstruction
Semantic Hashing is a popular family of methods for efficient similarity
search in large-scale datasets. In Semantic Hashing, documents are encoded as
short binary vectors (i.e., hash codes), such that semantic similarity can be
efficiently computed using the Hamming distance. Recent state-of-the-art
approaches have utilized weak supervision to train better performing hashing
models. Inspired by this, we present Semantic Hashing with Pairwise
Reconstruction (PairRec), which is a discrete variational autoencoder based
hashing model. PairRec first encodes weakly supervised training pairs (a query
document and a semantically similar document) into two hash codes, and then
learns to reconstruct the same query document from both of these hash codes
(i.e., pairwise reconstruction). This pairwise reconstruction enables our model
to encode local neighbourhood structures within the hash code directly through
the decoder. We experimentally compare PairRec to traditional and
state-of-the-art approaches, and obtain significant performance improvements in
the task of document similarity search.Comment: Accepted at SIGIR'2
Deep Binary Reconstruction for Cross-modal Hashing
With the increasing demand of massive multimodal data storage and
organization, cross-modal retrieval based on hashing technique has drawn much
attention nowadays. It takes the binary codes of one modality as the query to
retrieve the relevant hashing codes of another modality. However, the existing
binary constraint makes it difficult to find the optimal cross-modal hashing
function. Most approaches choose to relax the constraint and perform
thresholding strategy on the real-value representation instead of directly
solving the original objective. In this paper, we first provide a concrete
analysis about the effectiveness of multimodal networks in preserving the
inter- and intra-modal consistency. Based on the analysis, we provide a
so-called Deep Binary Reconstruction (DBRC) network that can directly learn the
binary hashing codes in an unsupervised fashion. The superiority comes from a
proposed simple but efficient activation function, named as Adaptive Tanh
(ATanh). The ATanh function can adaptively learn the binary codes and be
trained via back-propagation. Extensive experiments on three benchmark datasets
demonstrate that DBRC outperforms several state-of-the-art methods in both
image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.Comment: This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 201
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