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AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
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TLDR
AttnGAN as mentioned in this paper proposes an attentional generative network to synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description.Abstract:
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.read more
Citations
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Proceedings Article
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Aditya Ramesh,Mikhail Pavlov,Gabriel Goh,Scott Gray,Chelsea Voss,Alec Radford,Mark Chen,Ilya Sutskever +7 more
TL;DR: This work describes a simple approach based on a transformer that autoregressively models the text and image tokens as a single stream of data that is competitive with previous domain-specific models when evaluated in a zero-shot fashion.
Journal ArticleDOI
Image De-Raining Using a Conditional Generative Adversarial Network
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Book ChapterDOI
Stacked Cross Attention for Image-Text Matching
TL;DR: In this article, Liu et al. proposed a stacked cross-attention to discover the full latent alignments using both image regions and words in a sentence as context and infer image-text similarity, achieving state-of-the-art results on the MS-COCO and Flickr30K datasets.
References
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Generative Adversarial Nets
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.