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  <title>Fei Pan @ Univ. of Michigan</title>
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          <h2>Fei Pan</h2>
          feipan [at] umich.edu<br>
          <a href="./files/fei_cv.pdf">CV</a> | 
          <a href="https://scholar.google.com/citations?hl=en&user=VGE3DlYAAAAJ"> Google Scholar</a> 
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    <p align="justify">I am a Research Fellow in EECS at <a href="https://umich.edu/">University of Michigan</a>
     and fortunate to work with Prof. <a href="https://scholar.google.com/citations?user=uqWkLzMAAAAJ"> Stella X. Yu</a>. 
      My research lies in Computer Vision and Machine Learning. 
      I am interested in developing large-scale learning algorithms 
        for visual tasks with strong generalizability, vigorous robustness, and minimal human supervision.
      I obtained my Ph.D. degree in 2023 under the supervision from Prof. <a href="https://scholar.google.com/citations?user=XA8EOlEAAAAJ&hl=en">In So Kweon</a> at <a href="https://www.kaist.ac.kr/en/">KAIST</a>. 
      I've received <a href="https://www.qualcomm.com/research/university-relations/innovation-fellowship">Innovation Fellowship</a> from <a href="https://www.qualcomm.com/">Qualcomm</a> and 
        Ph.D. scholarship from <a href="https://www.bosch.com/">BOSCH</a> during my Ph.D. course. <br>
    <h3 id="research_interests">Research Interest</h3>
        <li>Grouping and Segmentation</li>
        <li>Large-Scale Vision & Language Models</li>
        <li>Adaptation & Generalization of Deep Learning Models</li>
      </ul>
    <h3 id="publications">Publications</h3>
    <!-- <h4 id="2023">2023</h4> -->
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              MoDA: Leveraging Motion Prior from Videos for Advancing Unsupervised Domain 
              Adaptation in Semantic Segmentation.<br>
              <strong>Fei Pan</strong>, Xu Yin, Seokju Lee, Axi Niu, Sungeui Yoon, In So Kweon.<br>
              IEEE/CVF Computer Vision and Pattern Recognition Conference Workshop (CVPRW), 2024. <a href="https://arxiv.org/pdf/2309.11711.pdf">[pdf]</a><a href="https://github.com/feipanir/MoDA/tree/main">[code]</a> <br>
              <i>Learning with Limited Labelled Data for Image and Video Understanding.</i><br>
              <b style="color:red;">Best Paper Award</b> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                  Unsupervised domain adaptation (UDA) is an effective approach to handle the 
                  lack of annotations in the target domain for the semantic segmentation task. 
                  In this work, we consider a more practical UDA setting where the target domain 
                  contains sequential frames of the unlabeled videos which are easy to collect
                  in practice. A recent study suggests self-supervised learning of the object motion 
                  from unlabeled videos with geometric constraints. We design a motion-guided domain 
                  adaptive semantic segmentation framework (MoDA), that utilizes self-supervised object 
                  motion to learn effective representations in the target domain. MoDA differs from 
                  previous methods that use temporal consistency regularization for the target domain frames. 
                  Instead, MoDA deals separately with the domain alignment on the foreground and 
                  background categories using different strategies. Specifically, MoDA contains foreground 
                  object discovery and foreground semantic mining to align the foreground domain gaps by 
                  taking the instance-level guidance from the object motion. 
                  Additionally, MoDA includes background adversarial training which contains a background 
                  category-specific discriminator to handle the background domain gaps. 
                  Experimental results on multiple benchmarks highlight the effectiveness of 
                  MoDA against existing approaches in the domain adaptive image segmentation and 
                  domain adaptive video segmentation. Moreover, MoDA is versatile and can be used in 
                  conjunction with existing state-of-the-art approaches to further improve performance.  
                <b>Key Words:</b> 
                Unsupervised Domain Adaptation, Semantic Segmentation, Motion Understanding, Geometric Learning.
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              ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object.<br>
              Chenshuang Zhang, <strong>Fei Pan</strong>,  Junmo Kim, In So Kweon, Chengzhi Mao.<br>
              IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024. <a href="https://arxiv.org/pdf/2403.18775.pdf">[pdf]</a> <a href="https://github.com/chenshuang-zhang/imagenet_d">[code]</a><br>
              <b style="color:red;">Highlight Poster</b> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                We establish rigorous benchmarks for visual perception robustness. 
                Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of 
                evaluation over synthetic corruptions, backgrounds, and textures, 
                yet those robustness benchmarks are restricted in specified variations and have low synthetic quality. 
                In this work, we introduce generative model as a data source for synthesizing hard images that 
                benchmark deep models' robustness. 
                Leveraging diffusion models, we are able to generate images with more diversified backgrounds, 
                textures, and materials than any prior work, where we term this benchmark as ImageNet-D.
                Experimental results show that ImageNet-D results in a significant accuracy drop 
                to a range of vision models, from the standard ResNet visual classifier to the 
                latest foundation models like CLIP and MiniGPT-4, significantly reducing their accuracy 
                by up to 64%. Our work suggests that diffusion models can be an effective source to test vision models.
                <b>Key Words:</b> 
                Diffusion Models, Large-Scale Vision and Language Models, Robustness and Generalization,.
              </span>
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              Zero-shot Building Attribute Extraction from Large-Scale Vision and Language Models.<br>
              <strong>Fei Pan</strong>, Sangryul Jeon, Brian Wang, Frank Mckenna, Stella Yu.<br>
              IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. <a href="https://arxiv.org/pdf/2312.12479.pdf">[pdf]</a> <a href="https://github.com/BuildingInfoSys/zeroshot_attribute_extraction">[code]</a> <a href="https://drive.google.com/file/d/1t8vMpSuvm7KgLj0hKt0I6jxb6gzlYrep/view">[poster]</a><br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Modern building recognition methods, exemplified by the BRAILS framework, 
                utilize supervised learning to extract information from satellite and 
                street-view images for image classification and semantic segmentation tasks. 
                However, each task module requires human-annotated data, 
                hindering the scalability and robustness to regional variations and annotation imbalances. 
                In response, we propose a new zero-shot workflow for building attribute extraction 
                that utilizes large-scale vision and language models to mitigate reliance on external annotations. 
                The proposed workflow contains two key components: image-level captioning and 
                segment-level captioning for the building images based on the vocabularies 
                pertinent to structural and civil engineering. 
                These two components generate descriptive captions by computing feature 
                representations of the image and the vocabularies, 
                and facilitating a semantic match between the visual and textual representations. 
                Consequently, our framework offers a promising avenue to enhance AI-driven 
                captioning for building attribute extraction in the structural and 
                civil engineering domains, ultimately reducing reliance on human annotations 
                while bolstering performance and adaptability. 
                <b>Key Words:</b> 
                Zero-shot Leanring, Building Attribute Extraction, Large-Scale Vision & Language Models.
              </span>
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                Masking-augmented Collaborative Domain Congregation for 
                Multi-target Domain Adaptation in Semantic Segmentation.<br>
                <strong>Fei Pan</strong><sup>*</sup>, Dong He<sup>*</sup>, Xu Yin, Chenshuang Zhang, Munchurl Kim.<br>
                IEEE Intelligent Vehicles Symposium (IV), 2024.<br>
                <b style="color:red;">Best Paper Nominated</b> <br>
                <span class="read-more-text">
                  <b>Abstract</b> <br>
                  This paper addresses the challenges in multi-target domain adaptive segmentation 
                  which aims at learning a single model that adapts to multiple diverse target domains. 
                  Existing methods show limited performance as they only consider the difference in visual appearance (style) 
                  while ignoring the (contextual) variations among multiple target domains. 
                  In contrast, we propose a novel approach termed Masking-augmented Collaborative Domain Congregation (MacDC) 
                  to handle the style gap and contextual gap altogether. 
                  The proposed MacDC comprises two key parts: collaborative domain congregation (CDC) and multi-context masking consistency (MCMC). 
                  Our CDC handles the style and contextual gaps among target domains by data mixing, which generates image-level and region-level 
                  intermediate domains among target domains. To further strengthen contextual alignment, 
                  our MCMC applies a masking-based self-supervised augmentation consistency that enforces the model's understanding of 
                  diverse contexts together.
                  MacDC directly learns a single model for multi-target domain adaptation without requiring multiple network training and subsequent distillation. 
                  Despite its simplicity, MacDC shows efficacy in mitigating the style and contextual gap among multiple target domains and demonstrates 
                  superior performance on multi-target domain adaptation for segmentation benchmarks compared to existing state-of-the-art approaches. 
                  <b>Key Words:</b> Multi-target Domain Adaptation, Semantic Segmentation, Masking Consistency, Self-supervised Data Augmentation.
                </span>
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              CCTV-Calib: a Toolbox to Calibrate Surveillance Cameras Around the Globe.<br>
              Francois Rameau, Jaesung Choe, <strong>Fei Pan</strong>, Seokju Lee, In So Kweon.<br>
              Machine Vision and Applications, 2023. <a href="https://trebuchet.public.springernature.app/get_content/52aff0d9-9afd-4117-a037-d0e8e34fd66c?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=nonoa_20231021&utm_content=10.1007/s00138-023-01476-1">[pdf]</a> <a href="https://github.com/rameau-fr/CCTV-Calib">[code]</a> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                In this paper, we propose CCTV-Calib, a user-friendly toolbox to calibrate 
                traffic cameras using satellite views.
                Specifically, CCTV-Calib can estimate the intrinsic and extrinsic
                parameters as well as the GPS location of one or multiple CCTV
                cameras in a few clicks. Previous surveillance camera calibration
                strategies rely on various assumptions on the camera parameters
                (e.g., absence of radial distortion), location, or detected objects
                in the scene. In contrast, our system is able to calibrate both
                perspective and fisheye cameras without restrictive structural
                or semantic assumptions. In fact, only a few correspondences
                between an image and its satellite view are sufficient to accurately
                calibrate a camera. Such kind of camera geo-localization and 
                calibration via satellite imaging has yet attracted narrow attention.
                As a result, most existing techniques naively rely on manually
                clicked keypoint correspondences between the satellite view and
                the CCTV image, leading to poor accuracy and repeatability. To
                cope with these limitations and to ease the calibration process, we
                propose an automated keypoints matching stage and a refinement
                process improving the accuracy of the computed parameters. Our
                toolbox has been qualitatively and quantitatively evaluated using
                synthetic and real data from various traffic cameras around the
                globe. We made these unique datasets freely available to the
                community. Finally, in order to illustrate the relevance of our
                calibration strategy, we demonstrate its applicability to 3D vehicle
                geolocalization. Our novel calibration pipeline is integrated in a
                easy to use GUI and is freely available via the following link:
                https://github.com/rameau-fr/CCTV-Calib.  
                <b>Key Words:</b> 
                Camera Calibration, CCTV, Vehicle Geolocalization.
              </span>
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              ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open 
              Compound Domain Adaptation in Semantic Segmentation.<br>
              <strong>Fei Pan</strong>, Sungsu Hur, Seokju Lee, Junsik Kim, In So Kweon.<br>
              European Conference on Computer Vision (ECCV), 2022. <a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136940228.pdf">[pdf]</a> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Open compound domain adaptation (OCDA) considers the target domain as the 
                compound of multiple unknown homogeneous subdomains. 
                The goal of OCDA is to minimize the domain gap between the labeled source domain 
                and the unlabeled compound target domain, which benefits the model generalization 
                to the unseen domains. Current OCDA for semantic segmentation methods adopt manual 
                domain separation and employ a single model to simultaneously adapt to all the 
                target subdomains. However, adapting to a target subdomain might hinder the model 
                from adapting to other dissimilar target subdomains, which leads to limited performance. 
                In this work, we introduce a multi-teacher framework with bidirectional photometric 
                mixing to separately adapt to every target subdomain. First, we present an automatic 
                domain separation to find the optimal number of subdomains. On this basis, we propose 
                a multi-teacher framework in which each teacher model uses bidirectional photometric 
                mixing to adapt to one target subdomain. Furthermore, we conduct an adaptive distillation 
                to learn a student model and apply consistency regularization to improve the student 
                generalization. Experimental results on benchmark datasets show the efficacy of the 
                proposed approach for both the compound domain and the open domains against existing 
                state-of-the-art approaches.
                <b>Key Words:</b> 
                Domain Adaptation, Open Compound Domain Adaptation, Semantic Segmentation, Multi-teacher Distillation.
              </span>
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              Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point 
              Supervised via Active Learning.<br>
              <strong>Fei Pan</strong>, Francois Rameau, Junsik Kim, In So Kweon. <br>
              arXiv, 2022. <a href="https://browse.arxiv.org/pdf/2206.00181.pdf">[pdf]</a><br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Training models dedicated to semantic segmentation requires a large amount 
                of pixel-wise annotated data. Due to their costly nature, these annotations
                might not be available for the task at hand. To alleviate this problem, 
                unsupervised domain adaptation approaches aim at aligning the feature 
                distributions between the labeled source and the unlabeled target data. 
                While these strategies lead to noticeable improvements, their effectiveness 
                remains limited. To guide the domain adaptation task more efficiently, previous 
                works attempted to include human interactions in this process under the form of 
                sparse single-pixel annotations in the target data. In this work, we propose a 
                new domain adaptation framework for semantic segmentation with annotated points 
                via active selection. First, we conduct an unsupervised domain adaptation of the 
                model; from this adaptation, we use an entropy-based uncertainty measurement for 
                target points selection. Finally, to minimize the domain gap, we propose a domain 
                adaptation framework utilizing these target points annotated by human annotators. 
                Experimental results on benchmark datasets show the effectiveness of our methods 
                against existing unsupervised domain adaptation approaches. The propose pipeline 
                is generic and can be included as an extra module to existing domain adaptation strategies.
                <b>Key Words:</b> 
                Active Learning, Unsupervised Domain Adaptation, Semantic Segmentation.
              </span>
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              Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation.<br>
              Seokju Lee, Francois Rameau, <strong>Fei Pan</strong>, In So Kweon.<br>
              International Conference on Computer Vision (ICCV), 2021. <a href="https://openaccess.thecvf.com/content/ICCV2021/papers/Lee_Attentive_and_Contrastive_Learning_for_Joint_Depth_and_Motion_Field_ICCV_2021_paper.pdf">[pdf]</a> <a href="https://github.com/SeokjuLee/Insta-DM">[code]</a> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Estimating the motion of the camera together with the 3D
                structure of the scene from a monocular vision system is a
                complex task that often relies on the so-called scene rigidity
                assumption. When observing a dynamic environment, this
                assumption is violated which leads to an ambiguity between
                the ego-motion of the camera and the motion of the objects.
                To solve this problem, we present a self-supervised learning 
                framework for 3D object motion field estimation from
                monocular videos. Our contributions are two-fold. First, we
                propose a two-stage projection pipeline to explicitly disentangle 
                the camera ego-motion and the object motions with
                dynamics attention module, called DAM. Specifically, we
                design an integrated motion model that estimates the motion 
                of the camera and object in the first and second warping stages, 
                respectively, controlled by the attention module
                through a shared motion encoder. Second, we propose an
                object motion field estimation through contrastive sample
                consensus, called CSAC, taking advantage of weak semantic
                prior (bounding box from an object detector) and geometric 
                constraints (each object respects the rigid body motion
                model). Experiments on KITTI, Cityscapes, and Waymo
                Open Dataset demonstrate the relevance of our approach
                and show that our method outperforms state-of-the-art algorithms 
                for the tasks of self-supervised monocular depth
                estimation, object motion segmentation, monocular scene
                flow estimation, and visual odometry.
                <b>Key Words:</b> 
                Motion Field Estimation, Monocular Depth Prediction, Geometric Learning.
              </span>
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              Two-phase Pseudo Label Densification for Self-training based Domain Adaptation.<br>
              Inkyu Shin, Sanghyun Woo, <strong>Fei Pan</strong>, In So Kweon.<br>
              European Conference on Computer Vision (ECCV), 2020. <a href="https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580528.pdf">[pdf]</a> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Recently, deep self-training approaches emerged as a powerful solution to 
                the unsupervised domain adaptation. The self-training scheme involves 
                iterative processing of target data; it generates target pseudo labels 
                and retrains the network. However, since only the confident predictions 
                are taken as pseudo labels, existing self-training approaches inevitably 
                produce sparse pseudo labels in practice. We see this is critical because 
                the resulting insufficient training-signals lead to a suboptimal, 
                error-prone model. In order to tackle this problem, we propose a novel 
                Two-phase Pseudo Label Densification framework, referred to as TPLD. 
                In the first phase, we use sliding window voting to propagate the confident 
                predictions, utilizing intrinsic spatial-correlations in the images. 
                In the second phase, we perform a confidence-based easy-hard classification. 
                For the easy samples, we now employ their full pseudo labels. 
                For the hard ones, we instead adopt adversarial learning to enforce hard-to-easy 
                feature alignment. To ease the training process and avoid noisy predictions, 
                we introduce the bootstrapping mechanism to the original self-training loss. 
                We show the proposed TPLD can be easily integrated into existing self-training 
                based approaches and improves the performance significantly. 
                Combined with the recently proposed CRST self-training framework, we achieve 
                new state-of-the-art results on two standard UDA benchmarks.
                <b>Key Words:</b> 
                Self-training, Domain Adaptation, Pseudo Label Correction.
              </span>
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              Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-supervision.<br>
              <strong>Fei Pan</strong>, Inkyu Shin, Francois Rameau, Seokju Lee, In So Kweon.<br>
              IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2020. <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.pdf">[pdf]</a> <a href="https://github.com/feipanir/IntraDA">[code]</a> <br>
              <b style="color:red;">Oral Presentation</b><br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Convolutional neural network-based approaches have achieved remarkable progress 
                in semantic segmentation. However, these approaches heavily rely on annotated 
                data which are labor intensive. To cope with this limitation, automatically 
                annotated data generated from graphic engines are used to train segmentation 
                models. However, the models trained from synthetic data are difficult to transfer 
                to real images. To tackle this issue, previous works have considered directly 
                adapting models from the source data to the unlabeled target data (to reduce the 
                inter-domain gap). Nonetheless, these techniques do not consider the large 
                distribution gap among the target data itself (intra-domain gap). 
                In this work, we propose a two-step self-supervised domain adaptation approach 
                to minimize the inter-domain and intra-domain gap together. 
                First, we conduct the inter-domain adaptation of the model; 
                from this adaptation, we separate the target domain into an easy and hard split 
                using an entropy-based ranking function. Finally, to decrease the intra-domain 
                gap, we propose to employ a self-supervised adaptation technique from the easy to 
                the hard split. Experimental results on numerous benchmark datasets highlight the 
                effectiveness of our method against existing state-of-the-art approaches. 
                The source code is available at https://github.com/feipanir/IntraDA.
                <b>Key Words:</b> 
                Domain Adaptation, Adversarial Training, Semantic Segmentation, Self-supervised Learning.
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              Variational Prototyping-Encoder: One-shot Learning with Prototypical Images.<br>
              Junsik Kim, Tae-hyun Oh, Seokju Lee, <strong>Fei Pan</strong>, In So Kweon.<br>
              IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2019. <a href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Variational_Prototyping-Encoder_One-Shot_Learning_With_Prototypical_Images_CVPR_2019_paper.pdf">[pdf]</a> <a href=" https://github.com/mibastro/VPE">[code]</a><br>
              <span class="read-more-text">
                <b>Abstract</b> 
                In daily life, graphic symbols, such as traffic signs and brand logos, 
                are ubiquitously utilized around us due to its intuitive expression beyond 
                language boundary. We tackle an open-set graphic symbol recognition problem 
                by one-shot classification with prototypical images as a single training example 
                for each novel class. We take an approach to learn a generalizable embedding space 
                for novel tasks. We propose a new approach called variational prototyping-encoder (VPE) 
                that learns the image translation task from real-world input images to their corresponding 
                prototypical images as a meta-task. As a result, VPE learns image similarity as well as 
                prototypical concepts which differs from widely used metric learning based approaches. 
                Our experiments with diverse datasets demonstrate that the proposed VPE performs favorably 
                against competing metric learning based one-shot methods. Also, our qualitative analyses 
                show that our meta-task induces an effective embedding space suitable for unseen data 
                representation.
                <b>Key Words:</b> 
                One-Shot Learning, Prototypical Learning, Variational Auto-encoder.
              </span>
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              Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks.<br>
              Sanghyuk Park, <strong>Fei Pan</strong>, Sunghun Kang, Chang D. Yoo.<br>
              Asian Conference on Computer Vision Workshops (ACCVW), 2016. <a href="https://link.springer.com/chapter/10.1007/978-3-319-54526-4_12">[pdf]</a> <br>
              <span class="read-more-text">
                <b>Abstract</b> 
                Statistics have shown that 20% of all road accidents are fatigue-related, 
                and drowsy detection is a car safety algorithm that can alert a snoozing driver 
                in hopes of preventing an accident. 
                This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) 
                network for learning effective features and detecting drowsiness given a RGB input 
                video of a driver. The DDD network consists of three deep networks for attaining global 
                robustness to background and environmental variations and learning local facial 
                movements and head gestures important for reliable detection. 
                The outputs of the three networks are integrated and fed to a softmax classifier for 
                drowsiness detection. Experimental results show that DDD achieves 73.06% detection accuracy on 
                NTHU-drowsy driver detection benchmark dataset.
                <b>Key Words:</b> 
                  Driver Drowsiness Detection, Representation learning.
              </span>
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      </ul>
    <strong>Academic Service</strong><br>
    <li>Journal Review: TPAMI, CVIU, Neurocomputing, Pattern Recognition Letters.</li>
    <li>Conference Review: CVPR, ICCV, ECCV, NeurIPS, AAAI. </li>
  <p align="center">
    <small><i>The three fundamental problems of computer vision are correspondence, correspondence, and correspondence! -- Takeo Kanade</i></small><br>
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