no code implementations • 24 Jan 2024 • Otto Brookes, Majid Mirmehdi, Colleen Stephens, Samuel Angedakin, Katherine Corogenes, Dervla Dowd, Paula Dieguez, Thurston C. Hicks, Sorrel Jones, Kevin Lee, Vera Leinert, Juan Lapuente, Maureen S. McCarthy, Amelia Meier, Mizuki Murai, Emmanuelle Normand, Virginie Vergnes, Erin G. Wessling, Roman M. Wittig, Kevin Langergraber, Nuria Maldonado, Xinyu Yang, Klaus Zuberbuhler, Christophe Boesch, Mimi Arandjelovic, Hjalmar Kuhl, Tilo Burghardt
We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment.
1 code implementation • 1 Dec 2023 • Shuchao Duan, Amirhossein Dadashzadeh, Alan Whone, Majid Mirmehdi
Beyond FER, pain estimation methods assess levels of intensity in pain expressions, however assessing the quality of all facial expressions is of critical value in health-related applications.
1 code implementation • 28 Nov 2023 • Hanyuan Wang, Majid Mirmehdi, Dima Damen, Toby Perrett
Previous one-stage action detection approaches have modelled temporal dependencies using only the visual modality.
1 code implementation • 11 Nov 2023 • Amirhossein Dadashzadeh, Shuchao Duan, Alan Whone, Majid Mirmehdi
The limited availability of labelled data in Action Quality Assessment (AQA), has forced previous works to fine-tune their models pretrained on large-scale domain-general datasets.
1 code implementation • CVPR 2023 • Toby Perrett, Saptarshi Sinha, Tilo Burghardt, Majid Mirmehdi, Dima Damen
We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties.
2 code implementations • 22 Feb 2023 • Chengxi Zeng, Xinyu Yang, David Smithard, Majid Mirmehdi, Alberto M Gambaruto, Tilo Burghardt
This paper presents a deep learning framework for medical video segmentation.
no code implementations • 25 Jan 2023 • Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi
Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide.
no code implementations • 6 Jan 2023 • Otto Brookes, Majid Mirmehdi, Hjalmar Kühl, Tilo Burghardt
We propose the first metric learning system for the recognition of great ape behavioural actions.
1 code implementation • 25 Oct 2022 • Hanyuan Wang, Majid Mirmehdi, Dima Damen, Toby Perrett
We obtain state-of-the-art performance on the challenging EPIC-KITCHENS-100 action detection as well as the standard THUMOS14 action detection benchmarks, and achieve improvement on the ActivityNet-1. 3 benchmark.
2 code implementations • 17 Aug 2022 • Chengxi Zeng, Xinyu Yang, Majid Mirmehdi, Alberto M Gambaruto, Tilo Burghardt
Our findings suggest that the proposed model can indeed enhance the TransUNet architecture via exploiting temporal information and improving segmentation performance by a significant margin.
1 code implementation • 17 Jul 2022 • Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs.
no code implementations • 14 Jul 2022 • Alessandro Masullo, Toby Perrett, Tilo Burghardt, Ian Craddock, Dima Damen, Majid Mirmehdi
We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) which takes advantage of learning using privileged information (LUPI).
1 code implementation • 30 Apr 2022 • Xinyu Yang, Tilo Burghardt, Majid Mirmehdi
We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors.
1 code implementation • 2 Jan 2022 • Hanyuan Wang, Dima Damen, Majid Mirmehdi, Toby Perrett
This incorporates a novel Voting Evidence Module to locate temporal boundaries, more accurately, where temporal contextual evidence is accumulated to predict frame-level probabilities of start and end action boundaries.
1 code implementation • 7 Dec 2021 • Amirhossein Dadashzadeh, Alan Whone, Majid Mirmehdi
Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons.
1 code implementation • 12 Nov 2021 • Mowen Xue, Theo Greenslade, Majid Mirmehdi, Tilo Burghardt
In particular, we show that the integration of a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network into standard recognition pipelines can considerably increase the detection efficacy in real-world settings.
no code implementations • 17 Sep 2021 • Faegheh Sardari, Björn Ommer, Majid Mirmehdi
Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features.
2 code implementations • CVPR 2021 • Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set.
no code implementations • 17 Dec 2020 • Amirhossein Dadashzadeh, Alan Whone, Michal Rolinski, Majid Mirmehdi
We evaluate our proposed method on a dataset of 25 PD patients, obtaining 72. 3% and 77. 1% top-1 accuracy on hand movement and gait tasks respectively.
1 code implementation • 14 Oct 2020 • Xinyu Yang, Majid Mirmehdi, Tilo Burghardt
In this paper we show that learning video feature spaces in which temporal cycles are maximally predictable benefits action classification.
no code implementations • 11 Aug 2020 • Faegheh Sardari, Adeline Paiement, Sion Hannuna, Majid Mirmehdi
We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data.
1 code implementation • 29 Jul 2020 • Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
This produces an initialisation for fine-tuning to target which is both context-agnostic and task-generalised.
no code implementations • 26 May 2020 • Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi
Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy.
no code implementations • 22 Oct 2019 • Farnoosh Heidarivincheh, Majid Mirmehdi, Dima Damen
In this work, we target detecting the completion moment of actions, that is the moment when the action's goal has been successfully accomplished.
no code implementations • 3 Oct 2019 • Alessandro Masullo, Tilo Burghardt, Toby Perrett, Dima Damen, Majid Mirmehdi
We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes.
no code implementations • 29 Aug 2019 • Xinyu Yang, Majid Mirmehdi, Tilo Burghardt
We propose the first multi-frame video object detection framework trained to detect great apes.
no code implementations • 21 Jun 2018 • Alessandro Masullo, Tilo Burghardt, Dima Damen, Sion Hannuna, Victor Ponce-López, Majid Mirmehdi
We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes.
2 code implementations • 14 Jun 2018 • Amirhossein Dadashzadeh, Alireza Tavakoli Targhi, Maryam Tahmasbi, Majid Mirmehdi
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.
Ranked #1 on Hand Gesture Segmentation on OUHANDS
no code implementations • 11 Jun 2018 • Víctor Ponce-López, Tilo Burghardt, Sion Hannunna, Dima Damen, Alessandro Masullo, Majid Mirmehdi
We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks.
1 code implementation • 17 May 2018 • Farnoosh Heidarivincheh, Majid Mirmehdi, Dima Damen
The paper proposes a joint classification-regression recurrent model that predicts completion from a given frame, and then integrates frame-level contributions to detect sequence-level completion moment.
no code implementations • 6 Oct 2017 • Farnoosh Heidarivincheh, Majid Mirmehdi, Dima Damen
Action completion detection is the problem of modelling the action's progression towards localising the moment of completion - when the action's goal is confidently considered achieved.
no code implementations • 27 Jul 2016 • Lili Tao, Tilo Burghardt, Majid Mirmehdi, Dima Damen, Ashley Cooper, Sion Hannuna, Massimo Camplani, Adeline Paiement, Ian Craddock
We present a new framework for vision-based estimation of calorific expenditure from RGB-D data - the first that is validated on physical gas exchange measurements and applied to daily living scenarios.
no code implementations • 14 Jun 2016 • Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuna, Tilo Burghardt, Lili Tao
Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.
no code implementations • 22 Dec 2015 • Toby Perrett, Majid Mirmehdi, Eduardo Dias
Knowledge of human presence and interaction in a vehicle is of growing interest to vehicle manufacturers for design and safety purposes.