830 research outputs found
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 Bidirectional Link Selection in Full Duplex MIMO Systems
In this paper, we consider a point to point full duplex (FD) MIMO
communication system. We assume that each node is equipped with an arbitrary
number of antennas which can be used for transmission or reception. With FD
radios, bidirectional information exchange between two nodes can be achieved at
the same time. In this paper we design bidirectional link selection schemes by
selecting a pair of transmit and receive antenna at both ends for
communications in each direction to maximize the weighted sum rate or minimize
the weighted sum symbol error rate (SER). The optimal selection schemes require
exhaustive search, so they are highly complex. To tackle this problem, we
propose a Serial-Max selection algorithm, which approaches the exhaustive
search methods with much lower complexity. In the Serial-Max method, the
antenna pairs with maximum "obtainable SINR" at both ends are selected in a
two-step serial way. The performance of the proposed Serial-Max method is
analyzed, and the closed-form expressions of the average weighted sum rate and
the weighted sum SER are derived. The analysis is validated by simulations.
Both analytical and simulation results show that as the number of antennas
increases, the Serial-Max method approaches the performance of the
exhaustive-search schemes in terms of sum rate and sum SER
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