1,602 research outputs found
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-networks: a deep recurrent neural network for sentences and a deep
convolutional network for images. These two sub-networks interact with each
other in a multimodal layer to form the whole m-RNN model. The effectiveness of
our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K,
Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In
addition, we apply the m-RNN model to retrieval tasks for retrieving images or
sentences, and achieves significant performance improvement over the
state-of-the-art methods which directly optimize the ranking objective function
for retrieval. The project page of this work is:
www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning
task significantly. More details are shown in Section 8 of the paper. The
code and related data are available at https://github.com/mjhucla/mRNN-CR ;.
arXiv admin note: substantial text overlap with arXiv:1410.109
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated according to this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216
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
Video Storytelling: Textual Summaries for Events
Bridging vision and natural language is a longstanding goal in computer
vision and multimedia research. While earlier works focus on generating a
single-sentence description for visual content, recent works have studied
paragraph generation. In this work, we introduce the problem of video
storytelling, which aims at generating coherent and succinct stories for long
videos. Video storytelling introduces new challenges, mainly due to the
diversity of the story and the length and complexity of the video. We propose
novel methods to address the challenges. First, we propose a context-aware
framework for multimodal embedding learning, where we design a Residual
Bidirectional Recurrent Neural Network to leverage contextual information from
past and future. Second, we propose a Narrator model to discover the underlying
storyline. The Narrator is formulated as a reinforcement learning agent which
is trained by directly optimizing the textual metric of the generated story. We
evaluate our method on the Video Story dataset, a new dataset that we have
collected to enable the study. We compare our method with multiple
state-of-the-art baselines, and show that our method achieves better
performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
In neural image captioning systems, a recurrent neural network (RNN) is
typically viewed as the primary `generation' component. This view suggests that
the image features should be `injected' into the RNN. This is in fact the
dominant view in the literature. Alternatively, the RNN can instead be viewed
as only encoding the previously generated words. This view suggests that the
RNN should only be used to encode linguistic features and that only the final
representation should be `merged' with the image features at a later stage.
This paper compares these two architectures. We find that, in general, late
merging outperforms injection, suggesting that RNNs are better viewed as
encoders, rather than generators.Comment: Appears in: Proceedings of the 10th International Conference on
Natural Language Generation (INLG'17
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