Search Results for author: Chi Harold Liu

Found 31 papers, 23 papers with code

Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

1 code implementation27 Feb 2023 Mixue Xie, Shuang Li, Rui Zhang, Chi Harold Liu

Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue.

Active Learning Domain Adaptation +2

Hierarchical Memory Pool Based Edge Semi-Supervised Continual Learning Method

no code implementations17 Jan 2023 Xiangwei Wang, Rui Han, Chi Harold Liu

In addition, in order to further reduce the computational overhead for unlabeled samples, EdgeHML leverages a progressive learning method.

Continual Learning

FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge

no code implementations4 Dec 2022 Yaxin Luopan, Rui Han, Qinglong Zhang, Chi Harold Liu, Guoren Wang

Upon training for a new task, the gradient integrator ensures the prevention of catastrophic forgetting and mitigation of negative knowledge transfer by effectively combining signature tasks identified from the past local tasks and other clients' current tasks through the global model.

Continual Learning Transfer Learning

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

1 code implementation22 Nov 2022 Mingjia Li, Binhui Xie, Shuang Li, Chi Harold Liu, Xinjing Cheng

However, previous methods often reckon on additional reference images of the same scenes taken from normal conditions, which are quite tough to collect in reality.

Domain Adaptation Semantic Segmentation

Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

1 code implementation2 Aug 2022 Wenxuan Ma, Jinming Zhang, Shuang Li, Chi Harold Liu, Yulin Wang, Wei Li

To alleviate these issues, we propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier tailored specifically for that domain.

Pseudo Label Unsupervised Domain Adaptation

Improving Transferability for Domain Adaptive Detection Transformers

1 code implementation29 Apr 2022 Kaixiong Gong, Shuang Li, Shugang Li, Rui Zhang, Chi Harold Liu, Qiang Chen

We implement the findings and the alignment modules into our adaptation method, and it benchmarks the DETR-style detector on the domain shift settings.

Object Detection Unsupervised Domain Adaptation

ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation

no code implementations26 Apr 2022 Zhenjie Yu, Kai Chen, Shuang Li, Bingfeng Han, Chi Harold Liu, Shuigen Wang

To be specific, ROMA could efficiently translate the unpaired nighttime infrared videos into fine-grained daytime visible ones, meanwhile maintain the spatiotemporal consistency via matching the cross-domain region similarity.


SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

1 code implementation19 Apr 2022 Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang, Guoren Wang

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain.

Semantic Segmentation Synthetic-to-Real Translation

Causality Inspired Representation Learning for Domain Generalization

1 code implementation CVPR 2022 Fangrui Lv, Jian Liang, Shuang Li, Bin Zang, Chi Harold Liu, Ziteng Wang, Di Liu

Specifically, we assume that each input is constructed from a mix of causal factors (whose relationship with the label is invariant across domains) and non-causal factors (category-independent), and only the former cause the classification judgments.

Domain Generalization Representation Learning

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

1 code implementation17 Feb 2022 Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, Chi Harold Liu

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.

reinforcement-learning Reinforcement Learning (RL)

LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision

no code implementations18 Dec 2021 Rui Han, Qinglong Zhang, Chi Harold Liu, Guoren Wang, Jian Tang, Lydia Y. Chen

The prior art sheds light on exploring the accuracy-resource tradeoff by scaling the model sizes in accordance to resource dynamics.

Knowledge Distillation Model Compression +1

Pareto Domain Adaptation

1 code implementation NeurIPS 2021 Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang

Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.

Domain Adaptation Image Classification +2

Active Learning for Domain Adaptation: An Energy-Based Approach

1 code implementation2 Dec 2021 Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, Guoren Wang

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.

Active Learning Transfer Learning +1

Semantic Concentration for Domain Adaptation

1 code implementation ICCV 2021 Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li

To tackle this issue, we propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions.

Domain Adaptation Transfer Learning

I2V-GAN: Unpaired Infrared-to-Visible Video Translation

1 code implementation2 Aug 2021 Shuang Li, Bingfeng Han, Zhenjie Yu, Chi Harold Liu, Kai Chen, Shuigen Wang

Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios.

object-detection Object Detection +1

Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

1 code implementation11 May 2021 Shuang Li, Binhui Xie, Bin Zang, Chi Harold Liu, Xinjing Cheng, Ruigang Yang, Guoren Wang

Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.

Self-Supervised Learning Semantic Segmentation

Dynamic Domain Adaptation for Efficient Inference

1 code implementation CVPR 2021 Shuang Li, Jinming Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy.

Domain Generalization Transfer Learning

Generalized Domain Conditioned Adaptation Network

1 code implementation23 Mar 2021 Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang, Guoren Wang

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision.

Domain Adaptation

Transferable Semantic Augmentation for Domain Adaptation

1 code implementation CVPR 2021 Shuang Li, Mixue Xie, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Wei Li

To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics.

Domain Adaptation

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

1 code implementation CVPR 2021 Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng

Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes.

Data Augmentation Image Classification +2

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

1 code implementation13 Dec 2020 Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, Chen Qin

Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability.

Semantic Segmentation

Social-Aware Incentive Mechanism for VehicularCrowdsensing by Deep Reinforcement Learning

1 code implementation IEEE Transactions on Intelligent Transportation Systems 2020 Yinuo Zhao, Chi Harold Liu

Vehicular crowdsensing (VCS) takes the advantage of vehicles’ mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers’ carried smart mobile devices and on-board sensors to accomplish challenging sensing tasks.

reinforcement-learning Reinforcement Learning (RL)

Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

1 code implementation4 Aug 2020 Shuang Li, Binhui Xie, Jiashu Wu, Ying Zhao, Chi Harold Liu, Zhengming Ding

In this paper, we propose a Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains.

Domain Adaptation Pseudo Label

Domain Conditioned Adaptation Network

1 code implementation14 May 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang

Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target.

Domain Adaptation

Deep Residual Correction Network for Partial Domain Adaptation

1 code implementation10 Apr 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Qi Wen, Limin Su, Gao Huang, Zhengming Ding

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain.

Partial Domain Adaptation

Experience-driven Networking: A Deep Reinforcement Learning based Approach

no code implementations17 Jan 2018 Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, Dejun Yang

Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control.

Continuous Control reinforcement-learning +1

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