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.
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
Ranked #5 on Text-to-Image Generation on COCO (using extra training data)
Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation.
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
To tackle these problems, we propose a novel Adversarial Knowledge Distillation framework for graph models named GraphAKD, which adversarially trains a discriminator and a generator to adaptively detect and decrease the discrepancy.
We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene.
Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing.
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Ranked #11 on Text-to-Image Generation on COCO (using extra training data)
Despite this, contrastive learning--which heavily relies on structural data augmentation and complicated training strategies--has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.