27,260 research outputs found
Conditional Image-Text Embedding Networks
This paper presents an approach for grounding phrases in images which jointly
learns multiple text-conditioned embeddings in a single end-to-end model. In
order to differentiate text phrases into semantically distinct subspaces, we
propose a concept weight branch that automatically assigns phrases to
embeddings, whereas prior works predefine such assignments. Our proposed
solution simplifies the representation requirements for individual embeddings
and allows the underrepresented concepts to take advantage of the shared
representations before feeding them into concept-specific layers. Comprehensive
experiments verify the effectiveness of our approach across three phrase
grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where
we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a
strong region-phrase embedding baseline.Comment: ECCV 2018 accepted pape
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas
We propose a new recurrent generative model for generating images from text
captions while attending on specific parts of text captions. Our model creates
images by incrementally adding patches on a "canvas" while attending on words
from text caption at each timestep. Finally, the canvas is passed through an
upscaling network to generate images. We also introduce a new method for
generating visual-semantic sentence embeddings based on self-attention over
text. We compare our model's generated images with those generated Reed et.
al.'s model and show that our model is a stronger baseline for text to image
generation tasks.Comment: CVC 201
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
Semi-supervised FusedGAN for Conditional Image Generation
We present FusedGAN, a deep network for conditional image synthesis with
controllable sampling of diverse images. Fidelity, diversity and controllable
sampling are the main quality measures of a good image generation model. Most
existing models are insufficient in all three aspects. The FusedGAN can perform
controllable sampling of diverse images with very high fidelity. We argue that
controllability can be achieved by disentangling the generation process into
various stages. In contrast to stacked GANs, where multiple stages of GANs are
trained separately with full supervision of labeled intermediate images, the
FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike
existing methods, which requires full supervision with paired conditions and
images, the FusedGAN can effectively leverage more abundant images without
corresponding conditions in training, to produce more diverse samples with high
fidelity. We achieve this by fusing two generators: one for unconditional image
generation, and the other for conditional image generation, where the two
partly share a common latent space thereby disentangling the generation. We
demonstrate the efficacy of the FusedGAN in fine grained image generation tasks
such as text-to-image, and attribute-to-face generation
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