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Found 13 papers, 8 papers with code
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
We show that MCM enables user control over the spatial layout of the image and leads to increased control over the image generation process.
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area.
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation.
Adversarial Text-to-Image Synthesis: A Review
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area.
Crossmodal Language Grounding in an Embodied Neurocognitive Model
From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration.
Improved Techniques for Training Single-Image GANs
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset.
Semantic Object Accuracy for Generative Text-to-Image Synthesis
To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption.
Ranked #53 on
Text-to-Image Generation
on MS COCO
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans.
Generating Multiple Objects at Spatially Distinct Locations
Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.
Ranked #64 on
Text-to-Image Generation
on MS COCO
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks
This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.
Image Generation and Translation with Disentangled Representations
We train an encoder to encode images into these representations and use a small amount of labeled data to specify what kind of information should be encoded in the disentangled part.
Inferencing Based on Unsupervised Learning of Disentangled Representations
Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.
Ranked #5 on
Unsupervised Image Classification
on MNIST
