Trending Research
PaddleNLP
PaddlePaddle/PaddleNLP
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COLING (TextGraphs) 2020
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.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing.
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
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.
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Extracting Triangular 3D Models, Materials, and Lighting From Images
NVlabs/nvdiffrec
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We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Automated Crossword Solving
albertkx/berkeley-crossword-solver
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ACL 2022
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles.
Ivy: Templated Deep Learning for Inter-Framework Portability
ivy-dl/ivy
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We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
implus/um-mae
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Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-training efficiency and fine-tuning accuracy.
Ranked #16 on
Object Detection
on COCO minival
Towards Unified Keyframe Propagation Models
runwayml/guided-inpainting
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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.
Zero-Shot Text-to-Image Generation
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)
