Trending Research
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
facebookresearch/fairseq
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EMNLP 2021
We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks.
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 2021
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.
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.
Vision Transformer Adapter for Dense Predictions
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
Towards An End-to-End Framework for Flow-Guided Video Inpainting
MCG-NKU/E2FGVI
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Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories.
Ranked #1 on
Video Inpainting
on YouTube-VOS 2018 val
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 #5 on
Zero-Shot Text-to-Image Generation
on COCO
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances.
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.
