Keyan Chen (陈科研)

Keyan Chen (陈科研)

Beihang University

👋 About Me

I am a Ph.D. student in the LEVIR lab. at Beihang University, advised by Prof. Zhenwei Shi and Prof. Zhengxia Zou. Before that, I received my B.S. and M.S. degree in Image Processing Center at Beihang University.

My research interests lie in the deep learning, computer vision, remote sensing image processing, and multimodal.

🔥 News

  • 2023.05: A paper is accepted by TGRS (IF=8.2) in 2023.
  • 2023.02: A paper is accepted by CVPR in 2023.
  • 2023.01: My google scholar citations have exceeded 1000! 🎉🎉
  • 2023.01: Our survey on object detection is accepted by Proceedings of the IEEE (IF=20.6).
  • 2022.12: Three papers are accepted by TGRS (IF=8.2) in 2022.

📝 Publications

sym

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Arxiv, 2023
Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi
[Arxiv] [Github] [Page] [Demo]

We consider designing an automated instance segmentation approach for remote sensing images based on the SAM foundation model, incorporating semantic category information with prompt learning.
sym

Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2023
Keyan Chen, Wenyuan Li, Sen Lei, Jianqi Chen, Xiaolong Jiang, Zhengxia Zou, and Zhenwei Shi
[Arxiv] [Github] [Page] [Demo]

We propose a new super-resolution framework based on context interaction in implicit function space for learning continuous representations of remote sensing images, called FunSR, which consists of three main components: a functional representor, a functional interactor, and a functional parser.
sym

OvarNet: Towards Open-vocabulary Object Attribute Recognition
Computer Vision and Pattern Recognition Conference (CVPR), 2023
Keyan Chen, Xiaolong Jiang, Yao Hu, Xu Tang, Yan Gao, Jianqi Chen and Weidi Xie
[Arxiv] [Github] [Page]

We consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.
sym

Object Detection in 20 Years: A Survey
Proceedings of the IEEE (P IEEE), 2023
Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo and Jieping Ye
[PDF] [Github] [Page]

This paper extensively reviews the fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods.
sym

Resolution-agnostic Remote Sensing Scene Classification with Implicit Neural Representations
IEEE Geoscience and Remote Sensing Letters (GRSL), 2022
Keyan Chen, Wenyuan Li, Jianqi Chen, Zhengxia Zou and Zhenwei Shi
[PDF] [Github] [Page]

We propose a novel scene classification method with scale and resolution adaptation ability. Unlike previous CNNbased methods that make predictions based on rasterized image inputs, the proposed method converts the images as continuous functions with INRs optimization and then performs classification within the function space.
sym

Geographical Supervision Correction for Remote Sensing Representation Learning
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022
Wenyuan Li, Keyan Chen, and Zhenwei Shi
[PDF]

We propose a Geographical supervision Correction method (GeCo) for remote sensing representation learning. Deviated geographical supervision generated by GLC products can be corrected adaptively using the correction matrix during network pre-training and joint optimization process is designed to simultaneously update the correction matrix and network parameters.
sym

A Degraded Reconstruction Enhancement-based Method for Tiny Ship Detection in Remote Sensing Images with A New Large-scale Dataset
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022
Jianqi Chen, Keyan Chen, Hao Chen, Zhengxia Zou and Zhenwei Shi
[PDF] [Github] [Dataset]

We propose a tiny ship detection method namely, Degraded Reconstruction Enhancement Network (DRENet), for medium-resolution remote sensing images, and introduce Levir-Ship, which contains 3876 GF-1/GF-6 multi-spectral images and over 3K tiny ship instances.
sym

Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022
Jianqi Chen, Keyan Chen, Hao Chen, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi
[PDF] [Github]

We propose an asynchronous contrastive learning-based method for effective fine-grained ship classification, which refers to as "Push-and-Pull Network (P2Net)", includes a "push-out stage" and a "pull-in stage", where the first stage forces all the instances to be de-correlated and then the second one groups them into each subclass.
sym

Geographical Knowledge-Driven Representation Learning for Remote Sensing Images
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
Wenyuan Li, Keyan Chen, Hao Chen and Zhenwei Shi
[PDF] [Github]

We propose a Geographical Knowledge-driven Representation learning method for remote sensing images (GeoKR), improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associated with each remote sensing image are regarded as geographical knowledge to provide supervision for representation learning and network pre-training.
sym

Building Extraction from Remote Sensing Images with Sparse Token Transformers
Remote Sensing, 2021
Keyan Chen, Zhengxia Zou and Zhenwei Shi
[PDF] [Github] [Page] [Demo]

We propose STT to explore the potential of using transformers for efficient building extraction. STT conducts an efficient dual-pathway transformer that learns the global semantic information in both their spatial and channel dimensions and achieves state-of-the-art accuracy on two building extraction benchmarks.

🎖 Honors and Awards

  • 2023 “National Scholarship of Ph.D.”, Beihang University (Top 1%)
  • 2022 “Outstanding M.S. Dissertation Award” of Beihang, Beihang University (Top 0.5%)
  • 2022 “Outstanding Graduates” of Beijing, Beijing (Top 1%)
  • 2021 Second Prize of “AVIC Scholarship”, Beihang University (Top 2%)
  • 2020 First Prize “Academic Scholarship for Postgraduates”, Beihang University (Top 5%)
  • 2019 First Prize “Academic Scholarship for Postgraduates”, Beihang University (Top 5%)
  • 2019 Top 10 of “Semi-supervised Video Object Segmentation Algorithm Competition”, Alibaba Tianchi (Top 10/947)
  • 2019 “Outstanding B.S. Dissertation Award” of Beihang and Beijing, Beijing, (Top 0.5%)
  • 2019 “Outstanding Graduates” of Beihang, Beihang University (Top 5%)
  • 2019 “Excellent Student” of Beihang, Beihang University (Top 1%)
  • 2018 Second Prize of the “9th Mechanical Innovation Design Competition”, Beijing
  • 2018 Second Prize of the “28th Fengru Cup Scientific and Technological Competition”, Beihang University
  • 2018 First Prize of the “Mathematics Modeling Competition”, Beihang University (Top 3%)
  • 2018 First Prize of “Lee Kum Kee Astronautics Scholarship”, Beihang University (Top 0.5%)
  • 2018 Special Scholarship of “Outstanding Academic Performance”, Beihang University (Top 2%)

📖 Educations

  • 2022.09 - Present, Ph.D. in Pattern Recognition and Intelligent System, Beihang University, China.
  • 2019.09 - 2022.01, M.S. in Pattern Recognition and Intelligent System, Beihang University, China.
  • 2015.09 - 2019.06, B.S. in Image Processing, Beihang University, China.