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.
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.
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing.
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
We evaluate our two-stream approach for inpainting tasks, where experiments show that it improves both the propagation of features within a single frame as required for image inpainting, as well as their propagation from keyframes to target frames.
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Ranked #5 on Zero-Shot Text-to-Image Generation on COCO
When fine-tuning on downstream tasks, a modality-specific adapter is used to introduce the data and tasks' prior information into the model, making it suitable for these tasks.
Ranked #1 on Semantic Segmentation on ADE20K val