Trending Research
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem.
#2 best model for
Panoptic Segmentation
on COCO panoptic
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
SOTA for Language Modelling on Penn Treebank (Word Level) (using extra training data)
COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION
Big Transfer (BiT): General Visual Representation Learning
• google-research/big_transfer
• 
We conduct detailed analysis of the main components that lead to high transfer performance.
SOTA for Image Classification on ObjectNet (Bounding Box) (using extra training data)
FEW-SHOT LEARNING FINE-GRAINED IMAGE CLASSIFICATION REPRESENTATION LEARNING
Surfboard: Audio Feature Extraction for Modern Machine Learning
We introduce Surfboard, an open-source Python library for extracting audio features with application to the medical domain.
Point2Mesh: A Self-Prior for Deformable Meshes
We optimize the network weights to deform an initial mesh to shrink-wrap a single input point cloud.
Instance-aware Image Colorization
Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly.
Skeleton-Aware Networks for Deep Motion Retargeting
• DeepMotionEditing/deep-motion-editing
• 
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
Unpaired Motion Style Transfer from Video to Animation
• DeepMotionEditing/deep-motion-editing
•
In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.
PyContrast
PyTorch implementation of Contrastive Learning methods
CONTRASTIVE LEARNING DATA AUGMENTATION INSTANCE SEGMENTATION OBJECT DETECTION REPRESENTATION LEARNING SELF-SUPERVISED IMAGE CLASSIFICATION SEMANTIC SEGMENTATION
Analyzing and Improving the Image Quality of StyleGAN
• rosasalberto/StyleGAN2-TensorFlow-2.x
• 
Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.