38 research outputs found
Generating Diverse and Meaningful Captions
Image Captioning is a task that requires models to acquire a multi-modal
understanding of the world and to express this understanding in natural
language text. While the state-of-the-art for this task has rapidly improved in
terms of n-gram metrics, these models tend to output the same generic captions
for similar images. In this work, we address this limitation and train a model
that generates more diverse and specific captions through an unsupervised
training approach that incorporates a learning signal from an Image Retrieval
model. We summarize previous results and improve the state-of-the-art on
caption diversity and novelty. We make our source code publicly available
online.Comment: Accepted for presentation at The 27th International Conference on
Artificial Neural Networks (ICANN 2018
Latent Variable Model for Multi-modal Translation
In this work, we propose to model the interaction between visual and textual
features for multi-modal neural machine translation (MMT) through a latent
variable model. This latent variable can be seen as a multi-modal stochastic
embedding of an image and its description in a foreign language. It is used in
a target-language decoder and also to predict image features. Importantly, our
model formulation utilises visual and textual inputs during training but does
not require that images be available at test time. We show that our latent
variable MMT formulation improves considerably over strong baselines, including
a multi-task learning approach (Elliott and K\'ad\'ar, 2017) and a conditional
variational auto-encoder approach (Toyama et al., 2016). Finally, we show
improvements due to (i) predicting image features in addition to only
conditioning on them, (ii) imposing a constraint on the minimum amount of
information encoded in the latent variable, and (iii) by training on additional
target-language image descriptions (i.e. synthetic data).Comment: Paper accepted at ACL 2019. Contains 8 pages (11 including
references, 13 including appendix), 6 figure
Generating Diverse and Meaningful Captions: Unsupervised Specificity Optimization for Image Captioning
Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty.
We make our source code publicly available online: https://github.com/AnnikaLindh/Diverse_and_Specific_Image_Captionin
Image Captioning with Context-Aware Auxiliary Guidance
Image captioning is a challenging computer vision task, which aims to
generate a natural language description of an image. Most recent researches
follow the encoder-decoder framework which depends heavily on the previous
generated words for the current prediction. Such methods can not effectively
take advantage of the future predicted information to learn complete semantics.
In this paper, we propose Context-Aware Auxiliary Guidance (CAAG) mechanism
that can guide the captioning model to perceive global contexts. Upon the
captioning model, CAAG performs semantic attention that selectively
concentrates on useful information of the global predictions to reproduce the
current generation. To validate the adaptability of the method, we apply CAAG
to three popular captioners and our proposal achieves competitive performance
on the challenging Microsoft COCO image captioning benchmark, e.g. 132.2
CIDEr-D score on Karpathy split and 130.7 CIDEr-D (c40) score on official
online evaluation server