Search Results for author: Thomas B. Moeslund

Found 33 papers, 18 papers with code

SoccerNet 2023 Challenges Results

2 code implementations12 Sep 2023 Anthony Cioppa, Silvio Giancola, Vladimir Somers, Floriane Magera, Xin Zhou, Hassan Mkhallati, Adrien Deliège, Jan Held, Carlos Hinojosa, Amir M. Mansourian, Pierre Miralles, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdullah Kamal, Adrien Maglo, Albert Clapés, Amr Abdelaziz, Artur Xarles, Astrid Orcesi, Atom Scott, Bin Liu, Byoungkwon Lim, Chen Chen, Fabian Deuser, Feng Yan, Fufu Yu, Gal Shitrit, Guanshuo Wang, Gyusik Choi, Hankyul Kim, Hao Guo, Hasby Fahrudin, Hidenari Koguchi, Håkan Ardö, Ibrahim Salah, Ido Yerushalmy, Iftikar Muhammad, Ikuma Uchida, Ishay Be'ery, Jaonary Rabarisoa, Jeongae Lee, Jiajun Fu, Jianqin Yin, Jinghang Xu, Jongho Nang, Julien Denize, Junjie Li, Junpei Zhang, Juntae Kim, Kamil Synowiec, Kenji Kobayashi, Kexin Zhang, Konrad Habel, Kota Nakajima, Licheng Jiao, Lin Ma, Lizhi Wang, Luping Wang, Menglong Li, Mengying Zhou, Mohamed Nasr, Mohamed Abdelwahed, Mykola Liashuha, Nikolay Falaleev, Norbert Oswald, Qiong Jia, Quoc-Cuong Pham, Ran Song, Romain Hérault, Rui Peng, Ruilong Chen, Ruixuan Liu, Ruslan Baikulov, Ryuto Fukushima, Sergio Escalera, Seungcheon Lee, Shimin Chen, Shouhong Ding, Taiga Someya, Thomas B. Moeslund, Tianjiao Li, Wei Shen, Wei zhang, Wei Li, Wei Dai, Weixin Luo, Wending Zhao, Wenjie Zhang, Xinquan Yang, Yanbiao Ma, Yeeun Joo, Yingsen Zeng, Yiyang Gan, Yongqiang Zhu, Yujie Zhong, Zheng Ruan, Zhiheng Li, Zhijian Huang, Ziyu Meng

More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.

Action Spotting Camera Calibration +3

CL-MAE: Curriculum-Learned Masked Autoencoders

1 code implementation31 Aug 2023 Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi, Thomas B. Moeslund, Radu Tudor Ionescu

In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.

Representation Learning

Which Tokens to Use? Investigating Token Reduction in Vision Transformers

1 code implementation9 Aug 2023 Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund

While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets.

Classification Image Classification

Beyond AUROC & co. for evaluating out-of-distribution detection performance

2 code implementations26 Jun 2023 Galadrielle Humblot-Renaux, Sergio Escalera, Thomas B. Moeslund

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated.

Binary Classification Out-of-Distribution Detection +1

SoccerNet 2022 Challenges Results

7 code implementations5 Oct 2022 Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.

Action Spotting Camera Calibration +3

Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection

1 code implementation25 Sep 2022 Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah

In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.

Event Detection Fault Detection +1

MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

no code implementations20 Jul 2022 Malte Pedersen, Joakim Bruslund Haurum, Patrick Dendorfer, Thomas B. Moeslund

There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences.

Multi-Object Tracking Object

Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes

no code implementations15 Feb 2022 Simon B. Jensen, Thomas B. Moeslund, Søren J. Andreasen

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images.

Anomaly Detection Binary Classification +1

Video Transformers: A Survey

no code implementations16 Jan 2022 Javier Selva, Anders S. Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B. Moeslund, Albert Clapés

Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video.

Action Classification Self-Supervised Learning

Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

no code implementations15 Sep 2021 Galadrielle Humblot-Renaux, Letizia Marchegiani, Thomas B. Moeslund, Rikke Gade

In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.

Autonomous Navigation Decision Making +7

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

1 code implementation CVPR 2021 Joakim Bruslund Haurum, Thomas B. Moeslund

To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML.

Classification General Classification +2

Real-World Super-Resolution of Face-Images from Surveillance Cameras

no code implementations5 Feb 2021 Andreas Aakerberg, Kamal Nasrollahi, Thomas B. Moeslund

Experimental results on both real and artificially corrupted face images show that our method results in more detailed reconstructions with less noise compared to existing State-of-the-Art (SoTA) methods.

Generative Adversarial Network Image Quality Assessment +1

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

1 code implementation26 Jan 2021 Satya M. Muddamsetty, Mohammad N. S. Jahromi, Andreea E. Ciontos, Laura M. Fenoy, Thomas B. Moeslund

Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models.

Adversarial Attack Explainable artificial intelligence +1

SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

3 code implementations26 Nov 2020 Adrien Deliège, Anthony Cioppa, Silvio Giancola, Meisam J. Seikavandi, Jacob V. Dueholm, Kamal Nasrollahi, Bernard Ghanem, Thomas B. Moeslund, Marc Van Droogenbroeck

In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.

Action Spotting Boundary Detection +5

SIDU: Similarity Difference and Uniqueness Method for Explainable AI

2 code implementations4 Jun 2020 Satya M. Muddamsetty, Mohammad N. S. Jahromi, Thomas B. Moeslund

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability.

Multimodal and multiview distillation for real-time player detection on a football field

1 code implementation16 Apr 2020 Anthony Cioppa, Adrien Deliège, Noor Ul Huda, Rikke Gade, Marc Van Droogenbroeck, Thomas B. Moeslund

As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera.

Data Augmentation Knowledge Distillation +1

Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking

no code implementations3 Apr 2020 Seyed Mojtaba Marvasti-Zadeh, Hossein Ghanei-Yakhdan, Shohreh Kasaei, Kamal Nasrollahi, Thomas B. Moeslund

Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.

Semantic Segmentation Visual Object Tracking +1

Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage

no code implementations1 Apr 2020 Christoffer Bøgelund Rasmussen, Thomas B. Moeslund

We show that accuracy improvements can be made with more complex meta-architectures and speed can be optimised by decreasing the image size with only slight losses in accuracy.

Model Selection

Rain Removal in Traffic Surveillance: Does it Matter?

4 code implementations30 Oct 2018 Chris H. Bahnsen, Thomas B. Moeslund

We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow.

Instance Segmentation Rain Removal +2

The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos

no code implementations10 Sep 2018 Chris H. Bahnsen, Andreas Møgelmose, Thomas B. Moeslund

This tech report gives an introduction to two annotation toolboxes that enable the creation of pixel and polygon-based masks as well as bounding boxes around objects of interest.

General Classification TAG

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

no code implementations25 May 2018 Alireza Sepas-Moghaddam, Mohammad A. Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira

This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.

Face Recognition

Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks

no code implementations23 Jun 2016 Nattiya Kanhabua, Huamin Ren, Thomas B. Moeslund

In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e. g., spiky peaks for popular events, and periodicities for repetitive events.

Retrieval

A comprehensive study of sparse codes on abnormality detection

no code implementations13 Mar 2016 Huamin Ren, Hong Pan, Søren Ingvor Olsen, Thomas B. Moeslund

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes.

Anomaly Detection Event Detection

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