Fei Pan, KAIST

Fei Pan 潘飛
feipan at kaist dot ac dot kr

I am a Ph.D. student advised by Prof. In So Kweon at KAIST. I've received Qualcomm Innovation Fellowship and Ph.D. scholarship from Robert Bosch GmbH. My research background lies in computer vision and machine learning.

I have been working on some research topics including self-supervised learning, unsupervised learning, domain adaptation, and 3D motion prediction. Previously, I completed M.S. at KAIST and B.S. at Xidian University.

CV / Google Scholar / LinkedIn / ResearchGate

News

Jul 2022 - Our paper about Open Compound Domain Adaptation accepted in ECCV 2022.
Jun 2022 - We uploaded our recent work on combining Active Learning with Domain Adaptation for Semantic Segmentation task.
Jul 2021 - One paper accepted in ICCV 2021. This work deals with a joint framework on unsupervised learning of monocular depth and motion field estimation.
May 2021 - I started my internship at Robert Bosch GmbH working on domain adaptive multi-camera object detection.
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Publication

	MODA: Domain Adaptive Video Segmentation with Self-supervised Motion Understanding Fei Pan, Sohee Kim, Seokju Lee, In So Kweon under review, 2023
	ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation Fei Pan, Sungsu Hur, Seokju Lee, Junsik Kim, In So Kweon European Conference on Computer Vision (ECCV), 2022 poster / arXiv We design an automatic domain separation to best cluster the compound target domain and deploy a multi-teacher framework to adapt to all target subdomains separately.
	Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection Fei Pan, Francois Rameau, Junsik Kim, In So Kweon Preprint, 2022 arXiv Aiming at combining domain adaptation with active learning, we design a new adaptation framework for segmentation with annotated points via active selection.
	Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation Seokju Lee, Francois Rameau, Fei Pan, In So Kweon International Conference on Computer Vision (ICCV), 2021 arXiv / paper / supplementary A new two-stage projection pipeline is designed to explicitly disentangle the camera ego-motion and the object motions with the proposed dynamics attention module.
	Two-phase Pseudo Label Densification for Self-training based Domain Adaptation Inkyu Shin, Sanghyun Woo, Fei Pan, In So Kweon European Conference on Computer Vision (ECCV), 2020 arXiv / paper We propose a two-phase pseudo label densification network with a sliding window voting inside to propagate the confident predictions.
	Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, In So Kweon The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2020 (Oral Presentation) Qualcomm Innovation Fellowship arXiv / paper / project page / code / presentation / demo In contrast to previous methods which only consider the inter-domain alignment, a self-supervised domain adaptation is proposed to conduct the inter-domain and intra-domain alignment altogether.
	Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2019 arXiv / paper A variational prototyping-encoder is proposed to learn image similarity as well as prototypical concepts which differs from widely used metric learning based approaches.
	Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks Sanghyuk Park, Fei Pan, Sunghun Kang, Chang D. Yoo Asian Conference on Computer Vision (ACCV) Workshops, 2016 paper This paper proposes a deep drowsiness detection network for effective features and detecting drowsiness given an RGB input video of a driver.

Review Experience

Conference: CVPR, ICCV, WACV
Journal: Neurocomputing, Patten Recognition Letters

Thanks to the source code from this guy.