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Sofia Broomé
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Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
FM Serra Bragança, S Broomé, M Rhodin, S Björnsdóttir, V Gunnarsson, ...
Scientific reports 10 (1), 1-9, 2020
172020
Dynamics are Important for the Recognition of Equine Pain in Video
S Broomé, KB Gleerup, PH Andersen, H Kjellström
IEEE Conference on Computer Vision and Pattern Recognition, 2019
172019
Towards machine recognition of facial expressions of pain in horses
PH Andersen, S Broomé, M Rashid, J Lundblad, K Ask, Z Li, E Hernlund, ...
Animals 11 (6), 1643, 2021
152021
Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks
J Mänttäri, S Broomé, J Folkesson, H Kjellström
Computer Vision - ACCV 2020, 15th Asian Conference on Computer Vision, 2020
142020
Can a Machine Learn to See Horse Pain? An Interdisciplinary Approach Towards Automated Decoding of Facial Expressions of Pain in the Horse
PH Andersen, KB Gleerup, J Wathan, B Coles, H Kjellström, S Broomé, ...
Measuring Behavior 2018, 2018
102018
Automated detection of equine facial action units
Z Li, S Broomé, PH Andersen, H Kjellström
arXiv preprint arXiv:2102.08983, 2021
72021
Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses
S Broomé, K Ask, M Rashid-Engström, P Haubro Andersen, H Kjellström
PloS one 17 (3), e0263854, 2022
5*2022
What should I annotate? An automatic tool for finding video segments for EquiFACS annotation
M Rashid, S Broome, PH Andersen, KB Gleerup, YJ Lee
Measuring Behavior 2018, 2018
52018
Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation
M Rashid, S Broomé, K Ask, E Hernlund, PH Andersen, H Kjellström, ...
IEEE Winter Conference on Applications of Computer Vision, 2022
42022
hSMAL: Detailed horse shape and pose reconstruction for motion pattern recognition
C Li, N Ghorbani, S Broomé, M Rashid, MJ Black, E Hernlund, ...
arXiv preprint arXiv:2106.10102, 2021
42021
Objectively recognizing human activity in body-worn sensor data with (more or less) deep neural networks
S Broomé
Robotics, Perception and Learning; KTH Royal Institute of Technology, 2017
22017
Going deeper than tracking: a survey of computer-vision based recognition of animal pain and affective states
S Broomé, M Feighelstein, A Zamansky, GC Lencioni, PH Andersen, ...
arXiv preprint arXiv:2206.08405, 2022
12022
Learning Spatiotemporal Features in Low-Data and Fine-Grained Action Recognition with an Application to Equine Pain Behavior
S Broomé
KTH Royal Institute of Technology, 2022
2022
Recur, Attend or Convolve? Frame Dependency Modeling Matters for Cross-Domain Robustness in Action Recognition
S Broomé, E Pokropek, B Li, H Kjellström
arXiv preprint arXiv:2112.12175, 2021
2021
[Re] Unsupervised Scalable Representation Learning for Multivariate Time Series
F Liljefors, MM Sorkhei, S Broomé
ReScience C 6 (NeurIPS 2019 Reproducibility Challenge), 2020
2020
A PDE Perspective on Climate Modeling
S Broomé, J Ridenour
Department of Mathematics, KTH Royal Institute of Technology, 2014
2014
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Articles 1–16