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SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Surgical interventions, particularly in neurology, represent complex and high-stakes scenarios that impose substantial cognitive burdens on surgical teams. Although deliberate education and practice can enhance cognitive capabilities, surgical training opportunities remain limited due to patient safety concerns. To address these cognitive challenges in surgical training and operation, we propose S...Show More
SurgFC: Multimodal Surgical Function Calling Framework on the Demand of Surgeons
Zhen Chen;Xingjian Luo;Jinlin Wu;Danny T.M. Chan;Zhen Lei;Sebastien Ourselin;Hongbin Liu
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2024 | Conference Paper |
The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intellige...Show More
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
Zhen Chen;Zongming Zhang;Wenwu Guo;Xingjian Luo;Long Bai;Jinlin Wu;Hongliang Ren;Hongbin Liu
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward dif...Show More
PWISeg: Weakly-Supervised Surgical Instrument Instance Segmentation
2024 IEEE International Conference on Image Processing (ICIP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
AI-assisted operating room scene understanding is essential for the next generation of surgical interventions. Surgical instrument localization plays an important role in this context. However, existing instrument localization methods primarily focus on surgical instrument localization in endoscopy images and struggle with occlusions in broader operating room scenarios. In this work, we propose a ...Show More
SurgPLAN: Surgical Phase Localization Network for Phase Recognition
Xingjian Luo;You Pang;Zhen Chen;Jinlin Wu;Zongmin Zhang;Zhen Lei;Hongbin Liu
2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Surgical phase recognition is crucial to providing surgery understanding in smart operating rooms. Despite great progress in automatic surgical phase recognition, most existing methods are still restricted by two problems. First, these methods cannot capture discriminative visual features for each frame and motion information with simple 2D networks. Second, the frame-by-frame recognition paradigm...Show More
FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi;Ruben Tolosana;Ruben Vera-Rodriguez;Minchul Kim;Christian Rathgeb;Xiaoming Liu;Ivan DeAndres-Tame;Aythami Morales;Julian Fierrez;Javier Ortega-Garcia;Weisong Zhao;Xiangyu Zhu;Zheyu Yan;Xiao-Yu Zhang;Jinlin Wu;Zhen Lei;Suvidha Tripathi;Mahak Kothari;Md Haider Zama;Debayan Deb;Bernardo Biesseck;Pedro Vidal;Roger Granada;Guilherme Fickel;Gustavo Führ;David Menotti;Alexander Unnervik;Anjith George;Christophe Ecabert;Hatef Otroshi Shahreza;Parsa Rahimi;Sébastien Marcel;Ioannis Sarridis;Christos Koutlis;Georgia Baltsou;Symeon Papadopoulos;Christos Diou;Nicolò Di Domenico;Guido Borghi;Lorenzo Pellegrini;Enrique Mas-Candela;Ángela Sánchez-Pérez;Andrea Atzori;Gianni Fenu;Fadi Boutros;Mirko Marras;Naser Damer
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2024 | Conference Paper |
Cited by: Papers (6)
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use...Show More
FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi;Ruben Tolosana;Ruben Vera-Rodriguez;Minchul Kim;Christian Rathgeb;Xiaoming Liu;Ivan DeAndres-Tame;Aythami Morales;Julian Fierrez;Javier Ortega-Garcia;Weisong Zhao;Xiangyu Zhu;Zheyu Yan;Xiao-Yu Zhang;Jinlin Wu;Zhen Lei;Suvidha Tripathi;Mahak Kothari;Md Haider Zama;Debayan Deb;Bernardo Biesseck;Pedro Vidal;Roger Granada;Guilherme Fickel;Gustavo Führ;David Menotti;Alexander Unnervik;Anjith George;Christophe Ecabert;Hatef Otroshi Shahreza;Parsa Rahimi;Sébastien Marcel;Ioannis Sarridis;Christos Koutlis;Georgia Baltsou;Symeon Papadopoulos;Christos Diou;Nicolò Di Domenico;Guido Borghi;Lorenzo Pellegrini;Enrique Mas-Candela;Ángela Sánchez-Pérez;Andrea Atzori;Gianni Fenu;Fadi Boutros;Mirko Marras;Naser Damer
Year: 2024 | Conference Paper |
Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Weakly supervised person search aims to jointly detect and match persons with only bounding box annotations. Existing approaches typically focus on improving the features by exploring the relations of persons. However, scale variation problem is a more severe obstacle and under-studied that a person often owns images with different scales (resolutions). For one thing, small-scale images contain le...Show More
Unsupervised Graph Association for Person Re-Identification
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (80)
In this paper, we propose an unsupervised graph association (UGA) framework to learn the underlying viewinvariant representations from the video pedestrian tracklets. The core points of UGA are mining the underlying cross-view associations and reducing the damage of noise associations. To this end, UGA is adopts a two-stage training strategy: (1) intra-camera learning stage and (2) intercamera lea...Show More
Clustering and Dynamic Sampling Based Unsupervised Domain Adaptation for Person Re-Identification
Jinlin Wu;Shengcai Liao;Zhen lei;Xiaobo Wang;Yang Yang;Stan Z. Li
2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (27)
Person Re-Identification (Re-ID) has witnessed great improvements due to the advances of the deep convolutional neural networks (CNN). Despite this, existing methods mainly suffer from the poor generalization ability to unseen scenes because of the different characteristics between different domains. To address this issue, a Clustering and Dynamic Sampling (CDS) method is proposed in this paper, w...Show More
De-Mark GAN: Removing Dense Watermark with Generative Adversarial Network
Jinlin Wu;Hailin Shi;Shu Zhang;Zhen Lei;Yang Yang;Stan Z. Li
2018 International Conference on Biometrics (ICB)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
This paper mainly considers the MeshFace verification problem with dense watermarks. A dense watermark often covers the crucial parts of face photo, thus degenerating the performance in the existing face verification system. The key to solving it is to preserve the ID information while removing the dense watermark. In this paper, we propose an improved GAN model, named De-mark GAN, for MeshFace ve...Show More
Multi-modality Network with Visual and Geometrical Information for Micro Emotion Recognition
Jianzhu Guo;Shuai Zhou;Jinlin Wu;Jun Wan;Xiangyu Zhu;Zhen Lei;Stan Z. Li
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
Year: 2017 | Conference Paper |
Cited by: Papers (29)
Micro emotion recognition is a very challenging problem because of the subtle appearance variants among different facial expression classes. To deal with the mentioned problem, we proposed a multi-modality convolutional neural networks (CNNs) based on visual and geometrical information in this paper. The visual face image and structured geometry are embedded into a unified network and the recognit...Show More
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