activity
20162023
most citedExploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment

8 citations · 18 across the 6 of their papers we have counts for

collaborators

18 papers

cs.CV2023

Centre Stage: Centricity-based Audio-Visual Temporal Action Detection

Hanyuan Wang, Majid Mirmehdi, Dima Damen +1

Previous one-stage action detection approaches have modelled temporal dependencies using only the visual modality. In this paper, we explore different strategies to incorporate the…

cs.CV2022

Refining Action Boundaries for One-stage Detection

Hanyuan Wang, Majid Mirmehdi, Dima Damen +1

Current one-stage action detection methods, which simultaneously predict action boundaries and the corresponding class, do not estimate or use a measure of confidence in their boun…

cs.CV2022

TVNet: Temporal Voting Network for Action Localization

Hanyuan Wang, Dima Damen, Majid Mirmehdi +1

We propose a Temporal Voting Network (TVNet) for action localization in untrimmed videos. This incorporates a novel Voting Evidence Module to locate temporal boundaries, more accur…

cs.CV2021

Temporal-Relational CrossTransformers for Few-Shot Action Recognition

Toby Perrett, Alessandro Masullo, Tilo Burghardt +2

We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous fe…

cs.CV20208 cited

Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment

Amirhossein Dadashzadeh, Alan Whone, Michal Rolinski +1

Evaluating neurological disorders such as Parkinson's disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we pr…

cs.CV2020

Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation Learning

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. In particular, we propose a novel learni…