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Rethinking Global Context in Crowd Counting

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Abstract

This paper investigates the role of global context for crowd counting. Specifically, a pure transformer is used to extract features with global information from overlapping image patches. Inspired by classification, we add a context token to the input sequence, to facilitate information exchange with tokens corresponding to image patches throughout transformer layers. Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions, we propose a token-attention module (TAM) to recalibrate encoded features through channel-wise attention informed by the context token. Beyond that, it is adopted to predict the total person count of the image through regression-token module (RTM). Extensive experiments on various datasets, including ShanghaiTech, UCF-QNRF, JHU-CROWD++ and NWPU, demonstrate that the proposed context extraction techniques can significantly improve the performance over the baselines.

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Authors and Affiliations

  1. Computer Vision Lab, ETH Zürich, Zürich, 8092, Switzerland

    Guolei Sun, Danda Pani Paudel, Nikola Popovic & Luc Van Gool

  2. Institute for Infocomm Research, A*STAR, Singapore, 138632, Singapore

    Yun Liu

  3. Magic Leap, Zürich, 8050, Switzerland

    Thomas Probst

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  1. Guolei Sun
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Correspondence to Yun Liu.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Guolei Sun received the M.Sc. degree in computer science from King Abdullah University of Science and Technology, Saudi Arabia in 2018. From 2018 to 2019, he worked as a research engineer at the Inception Institute of Artificial Intelligence, UAE. Currently, he is a Ph.D. degree candidate at ETH Zurich, Switzerland under supervision of Prof. Luc Van Gool. He has published more than 20 papers in top journals and conferences such as TPAMI, CVPR, ICCV, and ECCV.

His research interests include computer vision and deep learning for tasks such as semantic segmentation, video understanding, and object counting.

Yun Liu received the B. Eng. and Ph.D. degrees in computer science from Nankai University, China in 2016 and 2020, respectively. Then, he worked with Prof. Luc Van Gool for one and a half years as a postdoctoral scholar at Computer Vision Lab, ETH Zürich, Switzerland. Currently, he is a senior scientist at Institute for Infocomm Research (I2R), A*STAR, Singapore.

His research interests include computer vision and machine learning.

Thomas Probst received the M. Sc. and Ph.D. degrees in computer science from Ulm University, Germany and ETH Zürich, Switzerland in 2014 and 2019, respectively. After that, he was a postdoctoral researcher at the Computer Vision Lab under Prof. Luc Van Gool at ETH Zürich, Switzerland.

His research interests include deep learning for geometry problems, and the perception of humans for robotics.

Danda Pani Paudel received the M.Sc. degree in computer vision and the Ph.D. degree in computer science from University of Bourgogne, France in 2012 and 2015, respectively. He worked as a research scholar at University of Strasbourg, France from 2013 to 2015, while devising global and local methods for 2D–3D registration problems. Currently, he is a researcher at the Computer Vision Lab, ETH Zürich, Switzerland, working with Prof. Luc Van Gool.

His research interests include computer vision, visual-SLAM, unsupervised learning and optimization methods.

Nikola Popovic is a Ph. D. degree candidate at the Computer Vision Lab at ETH Zürich, Switzerland. He has published a number of papers in major computer vision conferences.

His research interests include computer vision, machine learning and artificial intelligence.

Luc Van Gool received the B. Eng. degree in electromechanical engineering from the Katholieke Universiteit Leuven, Belgium in 1981. Currently, he is a professor at the Katholieke Universiteit Leuven in Belgium and the ETH in Zürich, Switzerland. He leads computer vision research at both places, and also teaches at both. He has been a program committee member of several major computer vision conferences. He received several Best Paper awards, won a David Marr Prize and a Koenderink Award, and was nominated Distinguished Researcher by the IEEE Computer Science committee. He is a co-founder of 10 spin-off companies.

His research interests include 3D reconstruction and modelling, object recognition, tracking, gesture analysis, and the combination of those.

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Sun, G., Liu, Y., Probst, T. et al. Rethinking Global Context in Crowd Counting. Mach. Intell. Res. 21, 640–651 (2024). https://doi.org/10.1007/s11633-023-1475-z

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