215,837 research outputs found
Towards Diverse and Natural Image Descriptions via a Conditional GAN
Despite the substantial progress in recent years, the image captioning
techniques are still far from being perfect.Sentences produced by existing
methods, e.g. those based on RNNs, are often overly rigid and lacking in
variability. This issue is related to a learning principle widely used in
practice, that is, to maximize the likelihood of training samples. This
principle encourages high resemblance to the "ground-truth" captions while
suppressing other reasonable descriptions. Conventional evaluation metrics,
e.g. BLEU and METEOR, also favor such restrictive methods. In this paper, we
explore an alternative approach, with the aim to improve the naturalness and
diversity -- two essential properties of human expression. Specifically, we
propose a new framework based on Conditional Generative Adversarial Networks
(CGAN), which jointly learns a generator to produce descriptions conditioned on
images and an evaluator to assess how well a description fits the visual
content. It is noteworthy that training a sequence generator is nontrivial. We
overcome the difficulty by Policy Gradient, a strategy stemming from
Reinforcement Learning, which allows the generator to receive early feedback
along the way. We tested our method on two large datasets, where it performed
competitively against real people in our user study and outperformed other
methods on various tasks.Comment: accepted in ICCV2017 as an Oral pape
Attention Correctness in Neural Image Captioning
Attention mechanisms have recently been introduced in deep learning for
various tasks in natural language processing and computer vision. But despite
their popularity, the "correctness" of the implicitly-learned attention maps
has only been assessed qualitatively by visualization of several examples. In
this paper we focus on evaluating and improving the correctness of attention in
neural image captioning models. Specifically, we propose a quantitative
evaluation metric for the consistency between the generated attention maps and
human annotations, using recently released datasets with alignment between
regions in images and entities in captions. We then propose novel models with
different levels of explicit supervision for learning attention maps during
training. The supervision can be strong when alignment between regions and
caption entities are available, or weak when only object segments and
categories are provided. We show on the popular Flickr30k and COCO datasets
that introducing supervision of attention maps during training solidly improves
both attention correctness and caption quality, showing the promise of making
machine perception more human-like.Comment: To appear in AAAI-17. See http://www.cs.jhu.edu/~cxliu/ for
supplementary materia
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