b'Jian Liang'

research

∙ 08/24/2023

Towards Realistic Unsupervised Fine-tuning with CLIP

The emergence of vision-language models (VLMs), such as CLIP, has spurre...

0 Jian Liang, et al. ∙

research

∙ 08/16/2023

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Controllable video generation has gained significant attention in recent...

0 Shengming Yin, et al. ∙

research

∙ 08/11/2023

Test-Time Adaptation for Backdoor Defense

Deep neural networks have played a crucial part in many critical domains...

0 Jiyang Guan, et al. ∙

research

∙ 07/18/2023

Rumor Detection with Diverse Counterfactual Evidence

The growth in social media has exacerbated the threat of fake news to in...

0 Kaiwei Zhang, et al. ∙

research

∙ 07/14/2023

TALL: Thumbnail Layout for Deepfake Video Detection

The growing threats of deepfakes to society and cybersecurity have raise...

0 Yuting Xu, et al. ∙

research

∙ 07/14/2023

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

Unsupervised domain adaptation (UDA) has witnessed remarkable advancemen...

0 Dapeng Hu, et al. ∙

research

∙ 07/14/2023

Improving Zero-Shot Generalization for CLIP with Synthesized Prompts

With the growing interest in pretrained vision-language models like CLIP...

0 Zhengbo Wang, et al. ∙

research

∙ 07/06/2023

Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification

Test-time adaptation (TTA) is a technique aimed at enhancing the general...

0 Yongcan Yu, et al. ∙

research

∙ 05/31/2023

Multi-Epoch Learning for Deep Click-Through Rate Prediction Models

The one-epoch overfitting phenomenon has been widely observed in industr...

0 Zhaocheng Liu, et al. ∙

research

∙ 03/27/2023

A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

Machine learning methods strive to acquire a robust model during trainin...

0 Jian Liang, et al. ∙

research

∙ 03/27/2023

Mind the Label Shift of Augmentation-based Graph OOD Generalization

Out-of-distribution (OOD) generalization is an important issue for Graph...

0 Junchi Yu, et al. ∙

research

∙ 03/23/2023

Improving Generalization with Domain Convex Game

Domain generalization (DG) tends to alleviate the poor generalization ca...

2 Fangrui Lv, et al. ∙

research

∙ 03/22/2023

AUTO: Adaptive Outlier Optimization for Online Test-Time OOD Detection

Out-of-distribution (OOD) detection is a crucial aspect of deploying mac...

0 Puning Yang, et al. ∙

research

∙ 03/19/2023

AdaptGuard: Defending Against Universal Attacks for Model Adaptation

Model adaptation aims at solving the domain transfer problem under the c...

0 Lijun Sheng, et al. ∙

research

∙ 03/17/2023

Exploiting Semantic Attributes for Transductive Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes by generalizin...

0 Zhengbo Wang, et al. ∙

research

∙ 03/17/2023

MODIFY: Model-driven Face Stylization without Style Images

Existing face stylization methods always acquire the presence of the tar...

0 Yuhe Ding, et al. ∙

research

∙ 02/09/2023

MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection

Existing cross-domain keypoint detection methods always require accessin...

0 Yuhe Ding, et al. ∙

research

∙ 01/05/2023

Learning Feature Recovery Transformer for Occluded Person Re-identification

One major issue that challenges person re-identification (Re-ID) is the ...

0 Boqiang Xu, et al. ∙

research

∙ 10/21/2022

Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks

An off-the-shelf model as a commercial service could be stolen by model ...

0 Jiyang Guan, et al. ∙

research

∙ 10/01/2022

Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning

Federated learning aims to train models collaboratively across different...

7 Yujun Shi, et al. ∙

research

∙ 07/20/2022

NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

In this paper, we present NUWA-Infinity, a generative model for infinite...

4 Chenfei Wu, et al. ∙

research

∙ 06/19/2022

Finding Diverse and Predictable Subgraphs for Graph Domain Generalization

This paper focuses on out-of-distribution generalization on graphs where...

0 Junchi Yu, et al. ∙

research

∙ 06/10/2022

Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement

Heterogeneous Face Recognition (HFR) aims to match faces across differen...

0 Ziming Yang, et al. ∙

research

∙ 06/06/2022

A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT

In the field of car evaluation, more and more netizens choose to express...

0 Xingchen Liu, et al. ∙

research

∙ 06/01/2022

DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder

Recently most successful image synthesis models are multi stage process ...

0 Jie Shi, et al. ∙

research

∙ 05/29/2022

ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a l...

0 Yuhe Ding, et al. ∙

research

∙ 04/25/2022

Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction

Rich user behavior information is of great importance for capturing and ...

0 Xiaochen Li, et al. ∙

research

∙ 03/27/2022

Causality Inspired Representation Learning for Domain Generalization

Domain generalization (DG) is essentially an out-of-distribution problem...

13 Fangrui Lv, et al. ∙

research

∙ 12/20/2021

Reciprocal Normalization for Domain Adaptation

Batch normalization (BN) is widely used in modern deep neural networks, ...

67 Zhiyong Huang, et al. ∙

research

∙ 12/16/2021

META: Mimicking Embedding via oThers' Aggregation for Generalizable Person Re-identification

Domain generalizable (DG) person re-identification (ReID) aims to test a...

0 Boqiang Xu, et al. ∙

research

∙ 12/16/2021

UMAD: Universal Model Adaptation under Domain and Category Shift

Learning to reject unknown samples (not present in the source classes) i...

0 Jian Liang, et al. ∙

research

∙ 12/09/2021

Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning

Class Incremental Learning (CIL) aims at learning a multi-class classifi...

0 Yujun Shi, et al. ∙

research

∙ 12/08/2021

Pareto Domain Adaptation

Domain adaptation (DA) attempts to transfer the knowledge from a labeled...

6 Fangrui Lv, et al. ∙

research

∙ 12/06/2021

Incentive Compatible Pareto Alignment for Multi-Source Large Graphs

In this paper, we focus on learning effective entity matching models ove...

9 Jian Liang, et al. ∙

research

∙ 11/24/2021

NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

This paper presents a unified multimodal pre-trained model called NÜWA t...

26 Chenfei Wu, et al. ∙

research

∙ 08/19/2021

Fair and Consistent Federated Learning

Federated learning (FL) has gain growing interests for its capability of...

0 Sen Cui, et al. ∙

research

∙ 08/18/2021

Learning to Collaborate

In this paper, we focus on effective learning over a collaborative resea...

0 Sen Cui, et al. ∙

research

∙ 08/12/2021

Semantic Concentration for Domain Adaptation

Domain adaptation (DA) paves the way for label annotation and dataset bi...

2 Shuang Li, et al. ∙

research

∙ 08/11/2021

Semi-Supervised Domain Generalizable Person Re-Identification

Existing person re-identification (re-id) methods are stuck when deploye...

0 Lingxiao He, et al. ∙

research

∙ 08/05/2021

Hybrid Reasoning Network for Video-based Commonsense Captioning

The task of video-based commonsense captioning aims to generate event-wi...

14 Weijiang Yu, et al. ∙

research

∙ 06/22/2021

Give Me Your Trained Model: Domain Adaptive Semantic Segmentation without Source Data

Benefited from considerable pixel-level annotations collected from a spe...

0 Yuxi Wang, et al. ∙

research

∙ 06/09/2021

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

A central challenge in training classification models in the real-world ...

9 Mi Luo, et al. ∙

research

∙ 04/04/2021

Distill and Fine-tune: Effective Adaptation from a Black-box Source Model

To alleviate the burden of labeling, unsupervised domain adaptation (UDA...

0 Jian Liang, et al. ∙

research

∙ 03/25/2021

On Evolving Attention Towards Domain Adaptation

Towards better unsupervised domain adaptation (UDA). Recently, researche...

0 Kekai Sheng, et al. ∙

research

∙ 02/12/2021

Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

Contrastive self-supervised learning (CSL) leverages unlabeled data to t...

0 Yifan Zhang, et al. ∙

research

∙ 12/14/2020

Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a r...

0 Jian Liang, et al. ∙

research

∙ 12/13/2020

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Unsupervised domain adaptation challenges the problem of transferring kn...

6 Shuang Li, et al. ∙

research

∙ 11/01/2020

A Lane-Changing Prediction Method Based on Temporal Convolution Network

Lane-changing is an important driving behavior and unreasonable lane cha...

0 Yue Zhang, et al. ∙

research

∙ 10/15/2020

Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

Recent studies show that crowd-sourced Natural Language Inference (NLI) ...

0 Guanhua Zhang, et al. ∙

research

∙ 10/12/2020

Robust Finite Mixture Regression for Heterogeneous Targets

Finite Mixture Regression (FMR) refers to the mixture modeling scheme wh...

7 Jian Liang, et al. ∙

Jian Liang

Featured Co-authors