Trending Research
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
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.
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.
PaddleNLP
PaddlePaddle/PaddleNLP
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ACL ARR January 2022
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.
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.
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.
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.
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
hsouri/bayesiantransferlearning
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Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task.
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
In this paper, we present a novel two-pass approach to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model.
Ranked #3 on
Speech Recognition
on AISHELL-1
