58 research outputs found
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
It is well believed that video captioning is a fundamental but challenging
task in both computer vision and artificial intelligence fields. The prevalent
approach is to map an input video to a variable-length output sentence in a
sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless,
the training of RNN still suffers to some degree from vanishing/exploding
gradient problem, making the optimization difficult. Moreover, the inherently
recurrent dependency in RNN prevents parallelization within a sequence during
training and therefore limits the computations. In this paper, we present a
novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks
(dubbed as TDConvED) that fully employ convolutions in both encoder and decoder
networks for video captioning. Technically, we exploit convolutional block
structures that compute intermediate states of a fixed number of inputs and
stack several blocks to capture long-term relationships. The structure in
encoder is further equipped with temporal deformable convolution to enable
free-form deformation of temporal sampling. Our model also capitalizes on
temporal attention mechanism for sentence generation. Extensive experiments are
conducted on both MSVD and MSR-VTT video captioning datasets, and superior
results are reported when comparing to conventional RNN-based encoder-decoder
techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8%
to 67.2% on MSVD.Comment: AAAI 201
Supersymmetric SYK model and random matrix theory
In this paper, we investigate the effect of supersymmetry on the symmetry classification of random matrix theory ensembles. We mainly consider the random matrix behaviors in the N = 1 supersymmetric generalization of Sachdev-Ye-Kitaev (SYK) model, a toy model for two-dimensional quantum black hole with supersymmetric constraint. Some analytical arguments and numerical results are given to show that the statistics of the supersymmetric SYK model could be interpreted as random matrix theory ensembles, with a different eight-fold classification from the original SYK model and some new features. The time-dependent evolution of the spectral form factor is also investigated, where predictions from random matrix theory are governing the late time behavior of the chaotic hamiltonian with supersymmetry
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