6 citations · 15 across the 9 of their papers we have counts for
4 papers · 1 filter
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
Shengjia Zhang, Tiancheng Lin, Yi Xu
By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled target domain through explicit discr…
SLAP: Self-supervised Anomaly Detection with Adversarial Perturbation
Yizhou Wang, Can Qin, Rongzhe Wei +3
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed…
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling
Tarik Arici, Mehmet Saygin Seyfioglu, Tal Neiman +5
Vision-and-Language Pre-training (VLP) improves model performance for downstream tasks that require image and text inputs. Current VLP approaches differ on (i) model architecture (…
Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation
Chen Shi, Xiangtai Li, Yanran Wu +2
Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic contex…