86,159 research outputs found
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015
Multimodal Machine Learning workshop paper
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition 2016.
Fixed titl
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
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
We introduce a model for bidirectional retrieval of images and sentences
through a multi-modal embedding of visual and natural language data. Unlike
previous models that directly map images or sentences into a common embedding
space, our model works on a finer level and embeds fragments of images
(objects) and fragments of sentences (typed dependency tree relations) into a
common space. In addition to a ranking objective seen in previous work, this
allows us to add a new fragment alignment objective that learns to directly
associate these fragments across modalities. Extensive experimental evaluation
shows that reasoning on both the global level of images and sentences and the
finer level of their respective fragments significantly improves performance on
image-sentence retrieval tasks. Additionally, our model provides interpretable
predictions since the inferred inter-modal fragment alignment is explicit
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
The inability to interpret the model prediction in semantically and visually
meaningful ways is a well-known shortcoming of most existing computer-aided
diagnosis methods. In this paper, we propose MDNet to establish a direct
multimodal mapping between medical images and diagnostic reports that can read
images, generate diagnostic reports, retrieve images by symptom descriptions,
and visualize attention, to provide justifications of the network diagnosis
process. MDNet includes an image model and a language model. The image model is
proposed to enhance multi-scale feature ensembles and utilization efficiency.
The language model, integrated with our improved attention mechanism, aims to
read and explore discriminative image feature descriptions from reports to
learn a direct mapping from sentence words to image pixels. The overall network
is trained end-to-end by using our developed optimization strategy. Based on a
pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we
conduct sufficient experiments to demonstrate that MDNet outperforms
comparative baselines. The proposed image model obtains state-of-the-art
performance on two CIFAR datasets as well.Comment: CVPR2017 Ora
Unpaired Image Captioning via Scene Graph Alignments
Most of current image captioning models heavily rely on paired image-caption
datasets. However, getting large scale image-caption paired data is
labor-intensive and time-consuming. In this paper, we present a scene
graph-based approach for unpaired image captioning. Our framework comprises an
image scene graph generator, a sentence scene graph generator, a scene graph
encoder, and a sentence decoder. Specifically, we first train the scene graph
encoder and the sentence decoder on the text modality. To align the scene
graphs between images and sentences, we propose an unsupervised feature
alignment method that maps the scene graph features from the image to the
sentence modality. Experimental results show that our proposed model can
generate quite promising results without using any image-caption training
pairs, outperforming existing methods by a wide margin.Comment: Accepted in ICCV 201
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Image captioning is a challenging problem owing to the complexity in
understanding the image content and diverse ways of describing it in natural
language. Recent advances in deep neural networks have substantially improved
the performance of this task. Most state-of-the-art approaches follow an
encoder-decoder framework, which generates captions using a sequential
recurrent prediction model. However, in this paper, we introduce a novel
decision-making framework for image captioning. We utilize a "policy network"
and a "value network" to collaboratively generate captions. The policy network
serves as a local guidance by providing the confidence of predicting the next
word according to the current state. Additionally, the value network serves as
a global and lookahead guidance by evaluating all possible extensions of the
current state. In essence, it adjusts the goal of predicting the correct words
towards the goal of generating captions similar to the ground truth captions.
We train both networks using an actor-critic reinforcement learning model, with
a novel reward defined by visual-semantic embedding. Extensive experiments and
analyses on the Microsoft COCO dataset show that the proposed framework
outperforms state-of-the-art approaches across different evaluation metrics
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