Trending Research
Zero-Shot Text-to-Image Generation
• openai/DALL-E
• 
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Monster Mash: A Single-View Approach to Casual 3D Modeling and Animation
Monster Mash is a new sketch-based modeling and animation tool that allows you to quickly sketch a character, inflate it into 3D, and promptly animate it.
ContraGAN: Contrastive Learning for Conditional Image Generation
NeurIPS 2020
• POSTECH-CVLab/PyTorch-StudioGAN
•
The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images.
Ranked #6 on
Conditional Image Generation
on CIFAR-10
(FID metric)
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs.
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
• whai362/PVT
•
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION
Simple multi-dataset detection
How do we build a general and broad object detection system?
INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION
First Order Motion Model for Image Animation
NeurIPS 2019
• AliaksandrSiarohin/first-order-model
•
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
• AI4Finance-LLC/FinRL-Library
• 
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
From Discrete to Continuous Convolution Layers
We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.
Convolution-Free Medical Image Segmentation using Transformers
• The-AI-Summer/self-attention-cv
•
We show that the proposed model can achieve segmentation accuracies that are better than the state of the art CNNs on three datasets.