447 research outputs found
Creativity: Generating Diverse Questions using Variational Autoencoders
Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.Comment: Accepted to CVPR 201
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
Video captioning in essential is a complex natural process, which is affected
by various uncertainties stemming from video content, subjective judgment, etc.
In this paper we build on the recent progress in using encoder-decoder
framework for video captioning and address what we find to be a critical
deficiency of the existing methods, that most of the decoders propagate
deterministic hidden states. Such complex uncertainty cannot be modeled
efficiently by the deterministic models. In this paper, we propose a generative
approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which
models the uncertainty observed in the data using latent stochastic variables.
Therefore, MS-RNN can improve the performance of video captioning, and generate
multiple sentences to describe a video considering different random factors.
Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with
both visual and textual features to capture a high-level representation. Then,
a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty
propagation by introducing latent variables. Experimental results on the
challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach
outperforms the state-of-the-art video captioning benchmarks
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