Search Results for author: Ruijia Xu

Found 9 papers, 6 papers with code

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories

1 code implementation8 Jun 2023 Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang

Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.

Domain Adaptation

Black-box Prompt Learning for Pre-trained Language Models

1 code implementation21 Jan 2022 Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang

Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.

text-classification Text Classification

Exploring Geometric Consistency for Monocular 3D Object Detection

no code implementations CVPR 2022 Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang

In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

Autonomous Driving Data Augmentation +4

An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation

no code implementations18 Dec 2019 Jihan Yang, Ruijia Xu, Ruiyu Li, Xiaojuan Qi, Xiaoyong Shen, Guanbin Li, Liang Lin

In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.

Position Segmentation +2

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift

1 code implementation CVPR 2018 Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan, Liang Lin

Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Multi-label Image Recognition by Recurrently Discovering Attentional Regions

no code implementations ICCV 2017 Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, Liang Lin

This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding.

General Classification Multi-Label Image Classification +1

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