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Guo-Xun Yuan - IEEE Xplore Author Profile

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Author details

Guo-Xun Yuan

Publications

3

Citations

244

Publications by Year

20122012

Co-Authors:

Chia-Hua HoChih-Jen LinKwan-Liu MaYingcai Wu

Guo-Xun Yuan

Affiliation

Computer Science Dept

University of California

Davis, CA

Publication Topics

Batch Method,
Coordinate Descent Method,
Dual Problem,
L1 Loss,
Linear Classifier,
Linear Support Vector Machine,
Test Accuracy,
Training Data,
Alternating Direction Method,
Amount Of Uncertainty,
Batch Learning,
Binary Classification

Biography

Guo-Xun Yuan received the B.S. degree in computer science from the National Tsinghua University, Hsinchu, Taiwan, and the M.S. degree in computer science from the National Taiwan University, Taipei, Taiwan. He is currently working towards the Ph.D. degree at the University of California Davis, Davis.,His research interest is large-scale data classification.(Based on document published on 3 April 2012).

Publications

3

Citations

244

Publications by Year

20122012

Co-Authors:

Chia-Hua Ho
Chih-Jen Lin
Kwan-Liu Ma
Yingcai Wu

Author's Published Works

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WICT, Peking University(3)
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IEEE Transactions on Multimedia(4)
2013 IEEE Conference on Computer Vision and Pattern Recognition(3)
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2021 IEEE/CVF International Conference on Computer Vision (ICCV)(3)
2008 IEEE Conference on Computer Vision and Pattern Recognition(2)
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Media(3)
Waikoloa, HI, USA(4)
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Semantic Video Moment Retrieval by Temporal Feature Perturbation and Refinement

Weitong Cai;Jiabo Huang;Jian Hu;Shaogang Gong;Hailin Jin;Yang Liu

2024 14th International Conference on Pattern Recognition Systems (ICPRS)

Year: 2024 | Conference Paper |

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Video moment retrieval (VMR) aims to locate temporal activities in untrimmed videos by sentence queries, facing a temporal bias problem. VMR models tend to over-rely on statistical regularities, instead of cross-modal semantics, and perform poorly against different distributions. Existing attempts clip/reorder video segments or overlook some samples, leading to sample integrity break and informati...Show More

Semantic Video Moment Retrieval by Temporal Feature Perturbation and Refinement

Weitong Cai;Jiabo Huang;Jian Hu;Shaogang Gong;Hailin Jin;Yang Liu

2024 14th International Conference on Pattern Recognition Systems (ICPRS)

Year: 2024 | Conference Paper |

Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models

Dezhao Luo;Jiabo Huang;Shaogang Gong;Hailin Jin;Yang Liu

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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Accurate video moment retrieval (VMR) requires universal visual-textual correlations that can handle unknown vocabulary and unseen scenes. However, the learned correlations are likely either biased when derived from a limited amount of moment-text data which is hard to scale up because of the prohibitive annotation cost (fully-supervised), or unreliable when only the video-text pairwise relationsh...Show More

Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models

Dezhao Luo;Jiabo Huang;Shaogang Gong;Hailin Jin;Yang Liu

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2024 | Conference Paper |

Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory

Ting Lei;Fabian Caba;Qingchao Chen;Hailin Jin;Yuxin Peng;Yang Liu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (12)

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Human Object Interaction (HOI) detection aims to localize and infer the relationships between a human and an object. Arguably, training supervised models for this task from scratch presents challenges due to the performance drop over rare classes and the high computational cost and time required to handle long-tailed distributions of HOIs in complex HOI scenes in realistic settings. This observati...Show More

Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory

Ting Lei;Fabian Caba;Qingchao Chen;Hailin Jin;Yuxin Peng;Yang Liu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training

Dezhao Luo;Jiabo Huang;Shaogang Gong;Hailin Jin;Yang Liu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (10)

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The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments. In this work, we explore multi-modal correlations derived from large-scale ima...Show More

Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training

Dezhao Luo;Jiabo Huang;Shaogang Gong;Hailin Jin;Yang Liu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos

Jielin Qiu;Franck Dernoncourt;Trung Bui;Zhaowen Wang;Ding Zhao;Hailin Jin

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials. However, Livestream tutorial videos are usually hours long, recorded, and uploaded to the Internet directly after the live sessions, making it hard for other people to catch up qu...Show More

LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos

Jielin Qiu;Franck Dernoncourt;Trung Bui;Zhaowen Wang;Ding Zhao;Hailin Jin

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

Zhenyu Wu;Haotao Wang;Zhaowen Wang;Hailin Jin;Zhangyang Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 4 | Journal Article |

Cited by: Papers (34)

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We investigate privacy-preserving, video-based action recognition in deep learning, a problem with growing importance in smart camera applications. A novel adversarial training framework is formulated to learn an anonymization transform for input videos such that the trade-off between target utility task performance and the associated privacy budgets is explicitly optimized on the anonymized video...Show More

Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

Zhenyu Wu;Haotao Wang;Zhaowen Wang;Hailin Jin;Zhangyang Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 4 | Journal Article |

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

Dan Ruta;Saeid Motiian;Baldo Faieta;Zhe Lin;Hailin Jin;Alex Filipkowski;Andrew Gilbert;John Collomosse

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (15)

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We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN...Show More

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

Dan Ruta;Saeid Motiian;Baldo Faieta;Zhe Lin;Hailin Jin;Alex Filipkowski;Andrew Gilbert;John Collomosse

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation

Jiabo Huang;Yang Liu;Shaogang Gong;Hailin Jin

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (29)

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Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos. For supervised model training, a temporal annotation of both the start and end time index of each video segment for a sentence (a video moment)...Show More

Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation

Jiabo Huang;Yang Liu;Shaogang Gong;Hailin Jin

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Time-Equivariant Contrastive Video Representation Learning

Simon Jenni;Hailin Jin

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (33)

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We introduce a novel self-supervised contrastive learning method to learn representations from unlabelled videos. Existing approaches ignore the specifics of input distortions, e.g., by learning invariance to temporal transformations. Instead, we argue that video representation should preserve video dynamics and reflect temporal manipulations of the input. Therefore, we exploit novel constraints t...Show More

Time-Equivariant Contrastive Video Representation Learning

Simon Jenni;Hailin Jin

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Magic Layouts: Structural Prior for Component Detection in User Interface Designs

Dipu Manandhar;Hailin Jin;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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We present Magic Layouts; a method for parsing screen-shots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn a prior over mobile UI layouts, encoding common spatial co-occurrence relation...Show More

Magic Layouts: Structural Prior for Component Detection in User Interface Designs

Dipu Manandhar;Hailin Jin;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Deep Image Comparator: Learning to Visualize Editorial Change

Alexander Black;Tu Bui;Hailin Jin;Vishy Swaminathan;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (6)

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We present a novel architecture for comparing a pair of images to identify image regions that have been subjected to editorial manipulation. We first describe a robust near-duplicate search, for matching a potentially manipulated image circulating online to an image within a trusted database of originals. We then describe a novel architecture for comparing that image pair, to localize regions that...Show More

Deep Image Comparator: Learning to Visualize Editorial Change

Alexander Black;Tu Bui;Hailin Jin;Vishy Swaminathan;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Superpixel Segmentation With Fully Convolutional Networks

Fengting Yang;Qian Sun;Hailin Jin;Zihan Zhou

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (154)

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In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization st...Show More

Superpixel Segmentation With Fully Convolutional Networks

Fengting Yang;Qian Sun;Hailin Jin;Zihan Zhou

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics

Simon Jenni;Hailin Jin;Paolo Favaro

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (26)

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We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability of learned features depends on what image neighborhood size is sufficient to discriminate different image transformations: The larger the required neighborhood size and the more global the image statistics that the feat...Show More

Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics

Simon Jenni;Hailin Jin;Paolo Favaro

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Screencast Tutorial Video Understanding

Kunpeng Li;Chen Fang;Zhaowen Wang;Seokhwan Kim;Hailin Jin;Yun Fu

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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Screencast tutorials are videos created by people to teach how to use software applications or demonstrate procedures for accomplishing tasks. It is very popular for both novice and experienced users to learn new skills, compared to other tutorial media such as text, because of the visual guidance and the ease of understanding. In this paper, we propose visual understanding of screencast tutorials...Show More

Screencast Tutorial Video Understanding

Kunpeng Li;Chen Fang;Zhaowen Wang;Seokhwan Kim;Hailin Jin;Yun Fu

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Visual Font Pairing

Shuhui Jiang;Zhaowen Wang;Aaron Hertzmann;Hailin Jin;Yun Fu

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 8 | Journal Article |

Cited by: Papers (7)

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This paper introduces the problem of automatic font pairing. Font pairing is an important design task that is difficult for novices. Given a font selection for one part of a document (e.g., header), our goal is to recommend a font to be used in another part (e.g., body) such that the two fonts used together look visually pleasing. There are three main challenges in font pairing. First, this is a f...Show More

Visual Font Pairing

Shuhui Jiang;Zhaowen Wang;Aaron Hertzmann;Hailin Jin;Yun Fu

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 8 | Journal Article |

Large-Scale Tag-Based Font Retrieval With Generative Feature Learning

Tianlang Chen;Zhaowen Wang;Ning Xu;Hailin Jin;Jiebo Luo

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Cited by: Papers (11)

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Font selection is one of the most important steps in a design workflow. Traditional methods rely on ordered lists which require significant domain knowledge and are often difficult to use even for trained professionals. In this paper, we address the problem of large-scale tag-based font retrieval which aims to bring semantics to the font selection process and enable people without expert knowledge...Show More

Large-Scale Tag-Based Font Retrieval With Generative Feature Learning

Tianlang Chen;Zhaowen Wang;Ning Xu;Hailin Jin;Jiebo Luo

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

An Internal Learning Approach to Video Inpainting

Haotian Zhang;Long Mai;Hailin Jin;Zhaowen Wang;Ning Xu;John Collomosse

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Cited by: Papers (48)

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We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images. In extending DIP to video we make two important contributions. First, we show that coherent video inpainting is poss...Show More

An Internal Learning Approach to Video Inpainting

Haotian Zhang;Long Mai;Hailin Jin;Zhaowen Wang;Ning Xu;John Collomosse

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Learning Video Representations From Correspondence Proposals

Xingyu Liu;Joon-Young Lee;Hailin Jin

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Cited by: Papers (44)

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Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular structure and complex dynamics. In this paper, we propose a novel neural network that learns video representations by aggregating information from potential correspondences. This network, named CPNet, can learn evolving 2D ...Show More

Learning Video Representations From Correspondence Proposals

Xingyu Liu;Joon-Young Lee;Hailin Jin

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search

John Collomosse;Tu Bui;Hailin Jin

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Cited by: Papers (38)

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LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet archit...Show More

LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search

John Collomosse;Tu Bui;Hailin Jin

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Disentangling Structure and Aesthetics for Style-Aware Image Completion

Andrew Gilbert;John Collomosse;Hailin Jin;Brian Price

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (10)

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Content-aware image completion or in-painting is a fundamental tool for the correction of defects or removal of objects in images. We propose a non-parametric in-painting algorithm that enforces both structural and aesthetic (style) consistency within the resulting image. Our contributions are two-fold: (1) we explicitly disentangle image structure and style during patch search and selection to en...Show More

Disentangling Structure and Aesthetics for Style-Aware Image Completion

Andrew Gilbert;John Collomosse;Hailin Jin;Brian Price

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Multi-task Adversarial Network for Disentangled Feature Learning

Yang Liu;Zhaowen Wang;Hailin Jin;Ian Wassell

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (42)

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We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify ea...Show More

Multi-task Adversarial Network for Disentangled Feature Learning

Yang Liu;Zhaowen Wang;Hailin Jin;Ian Wassell

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Michael J. Wilber;Chen Fang;Hailin Jin;Aaron Hertzmann;John Collomosse;Serge Belongie

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Cited by: Papers (79)

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Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize.,,This work is a step toward teaching machines how to categorize images in ways that...Show More

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Michael J. Wilber;Chen Fang;Hailin Jin;Aaron Hertzmann;John Collomosse;Serge Belongie

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Sketching with Style: Visual Search with Sketches and Aesthetic Context

John Collomosse;Tu Bui;Michael Wilber;Chen Fang;Hailin Jin

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Cited by: Papers (40)

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We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints. Our algorithm accepts a query as sketched shape, and a set of one or more contextual images specifying the desired visual aesthetic. A triplet network is used to learn a feature embedding capable of measuring style similarity independent of structure, delivering ...Show More

Sketching with Style: Visual Search with Sketches and Aesthetic Context

John Collomosse;Tu Bui;Michael Wilber;Chen Fang;Hailin Jin

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Spatial-Semantic Image Search by Visual Feature Synthesis

Long Mai;Hailin Jin;Zhe Lin;Chen Fang;Jonathan Brandt;Feng Liu

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Cited by: Papers (25) | Patents (1)

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The performance of image retrieval has been improved tremendously in recent years through the use of deep feature representations. Most existing methods, however, aim to retrieve images that are visually similar or semantically relevant to the query, irrespective of spatial configuration. In this paper, we develop a spatial-semantic image search technology that enables users to search for images w...Show More

Spatial-Semantic Image Search by Visual Feature Synthesis

Long Mai;Hailin Jin;Zhe Lin;Chen Fang;Jonathan Brandt;Feng Liu

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

Yinda Zhang;Shuran Song;Ersin Yumer;Manolis Savva;Joon-Young Lee;Hailin Jin;Thomas Funkhouser

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Cited by: Papers (158) | Patents (5)

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Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required f...Show More

Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

Yinda Zhang;Shuran Song;Ersin Yumer;Manolis Savva;Joon-Young Lee;Hailin Jin;Thomas Funkhouser

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

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