31,867 research outputs found
Describing Videos by Exploiting Temporal Structure
Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.Comment: Accepted to ICCV15. This version comes with code release and
supplementary materia
Areas of Attention for Image Captioning
We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.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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