World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
81
Citations
23670
World Ranking
1035
National Ranking
149

Overview

Xiaodan Liang is affiliated with Sun Yat-sen University in China and has an extensive publication record in the field of Computer Science. Their research focuses primarily on computer vision and pattern recognition, as well as artificial intelligence. Liang's contributions span multiple subfields, including control and systems engineering, computational mechanics, and aerospace engineering.

The scientist's work covers a wide range of topics, with particular emphasis on multimodal machine learning applications, domain adaptation and few-shot learning, topic modeling, advanced neural network applications, natural language processing techniques, advanced image and video retrieval techniques, and human pose and action recognition.

Frequent coauthors collaborating with Xiaodan Liang include Liang Lin, Xiaojun Chang, Hang Xu, and Jianhua Han. These partnerships reflect consistent teamwork on many research projects.

The scientist has published extensively in several venues, frequently contributing to:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Recent published papers by Xiaodan Liang include:

  • FILIP: Fine-grained Interactive Language-Image Pre-Training, 2021, arXiv (Cornell University)
  • Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • One Million Scenes for Autonomous Driving: ONCE Dataset, 2021, arXiv (Cornell University)
  • Knowledge Distillation via the Target-aware Transformer, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Exploring Inter-Channel Correlation for Diversity-preserved Knowledge Distillation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Overall, Xiaodan Liang's research activity is notable for its breadth across topics in machine learning and computer vision, with a consistent presence in leading academic conferences and repositories. Their collaborations and publication venues indicate active engagement within the scientific community specializing in artificial intelligence and related areas.

Best Publications

  • Is Faster R-CNN Doing Well for Pedestrian Detection?

    Liliang Zhang;Liang Lin;Xiaodan Liang;Kaiming He

  • Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

    Yunchao Wei;Jiashi Feng;Xiaodan Liang;Ming-Ming Cheng

  • Perceptual Generative Adversarial Networks for Small Object Detection

    Jianan Li;Xiaodan Liang;Yunchao Wei;Tingfa Xu

  • Toward controlled generation of text

    Zhiting Hu;Zichao Yang;Xiaodan Liang;Ruslan Salakhutdinov

  • Scale-Aware Fast R-CNN for Pedestrian Detection

    Jianan Li;Xiaodan Liang;Shengmei Shen;Tingfa Xu

  • STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation

    Yunchao Wei;Xiaodan Liang;Yunpeng Chen;Xiaohui Shen

  • Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning

    Xiaopeng Yan;Ziliang Chen;Anni Xu;Xiaoxi Wang

  • Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing

    Ke Gong;Xiaodan Liang;Dongyu Zhang;Xiaohui Shen

  • Semantic Object Parsing with Graph LSTM

    Xiaodan Liang;Xiaohui Shen;Jiashi Feng;Liang Lin

  • Toward Characteristic-Preserving Image-Based Virtual Try-On Network

    Bochao Wang;Huabin Zheng;Xiaodan Liang;Yimin Chen

  • Dual Motion GAN for Future-Flow Embedded Video Prediction

    Xiaodan Liang;Lisa Lee;Wei Dai;Eric P. Xing

  • Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark

    Xiaodan Liang;Ke Gong;Xiaohui Shen;Liang Lin

  • Instance-Level Human Parsing via Part Grouping Network.

    Ke Gong;Xiaodan Liang;Yicheng Li;Yimin Chen

  • Rethinking Knowledge Graph Propagation for Zero-Shot Learning

    Michael Kampffmeyer;Yinbo Chen;Xiaodan Liang;Hao Wang

  • Peak-Piloted Deep Network for Facial Expression Recognition

    Xiangyun Zhao;Xiaodan Liang;Luoqi Liu;Teng Li

  • Human Parsing with Contextualized Convolutional Neural Network

    Xiaodan Liang;Chunyan Xu;Xiaohui Shen;Jianchao Yang

  • Deep Human Parsing with Active Template Regression

    Xiaodan Liang;Si Liu;Xiaohui Shen;Jianchao Yang

  • Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

    Christy Y. Li;Xiaodan Liang;Zhiting Hu;Eric P. Xing

  • Poseidon: an efficient communication architecture for distributed deep learning on GPU clusters

    Hao Zhang;Zeyu Zheng;Shizhen Xu;Wei Dai

  • Adversarial Geometry-Aware Human Motion Prediction

    Liang-Yan Gui;Yu-Xiong Wang;Xiaodan Liang;José M. F. Moura

  • Proposal-Free Network for Instance-Level Object Segmentation

    Xiaodan Liang;Liang Lin;Yunchao Wei;Xiaohui Shen

  • Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

    Yuan Li;Xiaodan Liang;Zhiting Hu;Eric P. Xing

Frequent Co-Authors

Liang Lin
Liang Lin Sun Yat-sen University
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Shuicheng Yan
Shuicheng Yan National University of Singapore
Zhiting Hu
Zhiting Hu University of California, San Diego
Xiaohui Shen
Xiaohui Shen ByteDance
Jiashi Feng
Jiashi Feng ByteDance
Yunchao Wei
Yunchao Wei Beijing Jiaotong University
Zhenguo Li
Zhenguo Li Huawei Technologies (China)
Hao Zhang
Hao Zhang Simon Fraser University
Jianchao Yang
Jianchao Yang ByteDance

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