Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Deep learning based image description generation
Deep visual-semantic alignments for generating image descriptions
A Novel Semantic Attribute-Based Feature for Image Caption Generation
Guiding the Long-Short Term Memory Model for Image Caption Generation
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
A Powerful Generative Model Using Random Weights for the Deep Image Representation
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Visual Object Networks: Image Generation with Disentangled 3D Representation
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Computer Vision Report : Text to Image Synthesis
Generating Image Sequence from Description with LSTM Conditional GAN
Instance Map Based Image Synthesis With a Denoising Generative Adversarial Network
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
Depth Structure Preserving Scene Image Generation
Deep Learning for Image Captioning
Optimization of image description metrics using policy gradient methods
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Learning Deep Representations of Fine-Grained Visual Descriptions
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
Localization and Inhibition of Malicious Behaviors through a Model Checking based methodology
The segmented arch approach: A method for orthodontic treatment of a severe Class III open-bite malocclusion
Image Description using Visual Dependency Representations
When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach
Artificial Intelligence In Engineering:Past, Present And Future