6,738 research outputs found
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Multimodal representation learning is gaining more and more interest within
the deep learning community. While bilinear models provide an interesting
framework to find subtle combination of modalities, their number of parameters
grows quadratically with the input dimensions, making their practical
implementation within classical deep learning pipelines challenging. In this
paper, we introduce BLOCK, a new multimodal fusion based on the
block-superdiagonal tensor decomposition. It leverages the notion of block-term
ranks, which generalizes both concepts of rank and mode ranks for tensors,
already used for multimodal fusion. It allows to define new ways for optimizing
the tradeoff between the expressiveness and complexity of the fusion model, and
is able to represent very fine interactions between modalities while
maintaining powerful mono-modal representations. We demonstrate the practical
interest of our fusion model by using BLOCK for two challenging tasks: Visual
Question Answering (VQA) and Visual Relationship Detection (VRD), where we
design end-to-end learnable architectures for representing relevant
interactions between modalities. Through extensive experiments, we show that
BLOCK compares favorably with respect to state-of-the-art multimodal fusion
models for both VQA and VRD tasks. Our code is available at
https://github.com/Cadene/block.bootstrap.pytorch
Optimal Transmit Covariance for Ergodic MIMO Channels
In this paper we consider the computation of channel capacity for ergodic
multiple-input multiple-output channels with additive white Gaussian noise. Two
scenarios are considered. Firstly, a time-varying channel is considered in
which both the transmitter and the receiver have knowledge of the channel
realization. The optimal transmission strategy is water-filling over space and
time. It is shown that this may be achieved in a causal, indeed instantaneous
fashion. In the second scenario, only the receiver has perfect knowledge of the
channel realization, while the transmitter has knowledge of the channel gain
probability law. In this case we determine an optimality condition on the input
covariance for ergodic Gaussian vector channels with arbitrary channel
distribution under the condition that the channel gains are independent of the
transmit signal. Using this optimality condition, we find an iterative
algorithm for numerical computation of optimal input covariance matrices.
Applications to correlated Rayleigh and Ricean channels are given.Comment: 22 pages, 14 figures, Submitted to IEEE Transactions on Information
Theor
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