This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via hierarchical rearrangement.
Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image.
In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data.
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks.
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence.
To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector.
$A$Co$_2$Se$_2$ ($A$=K, Rb, Cs) is a homologue of the iron-based superconductor, $A$Fe$_2$Se$_2$.
Superconductivity Materials Science
In this paper, we study the properties of weak approximation with Brauer-Manin obstruction and the Hasse principle with Brauer-Manin obstruction for surfaces with respect to field extensions of number fields.
Number Theory Algebraic Geometry
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance.
The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis.
To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.
Object tracking has been studied for decades, but most of the existing works are focused on the short-term tracking.
Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.