7 research outputs found
Transformers for 1D Signals in Parkinson's Disease Detection from Gait
This paper focuses on the detection of Parkinson's disease based on the
analysis of a patient's gait. The growing popularity and success of Transformer
networks in natural language processing and image recognition motivated us to
develop a novel method for this problem based on an automatic features
extraction via Transformers. The use of Transformers in 1D signal is not really
widespread yet, but we show in this paper that they are effective in extracting
relevant features from 1D signals. As Transformers require a lot of memory, we
decoupled temporal and spatial information to make the model smaller. Our
architecture used temporal Transformers, dimension reduction layers to reduce
the dimension of the data, a spatial Transformer, two fully connected layers
and an output layer for the final prediction. Our model outperforms the current
state-of-the-art algorithm with 95.2\% accuracy in distinguishing a
Parkinsonian patient from a healthy one on the Physionet dataset. A key
learning from this work is that Transformers allow for greater stability in
results. The source code and pre-trained models are released in
https://github.com/DucMinhDimitriNguyen/Transformers-for-1D-signals-in-Parkinson-s-disease-detection-from-gait.gitComment: International Conference on Pattern Recognition (ICPR 2022
Multi-Object Tracking and Segmentation with a Space-Time Memory Network
We propose a method for multi-object tracking and segmentation that does not
require fine-tuning or per benchmark hyper-parameter selection. The proposed
tracker, MeNToS, addresses particularly the data association problem. Indeed,
the recently introduced HOTA metric, which has a better alignment with the
human visual assessment by evenly balancing detections and associations
quality, has shown that improvements are still needed for data association.
After creating tracklets using instance segmentation and optical flow, the
proposed method relies on a space-time memory network developed for one-shot
video object segmentation to improve the association of tracklets with temporal
gaps. We evaluated our tracker on KITTIMOTS and MOTSChallenge and show the
benefit of our data association strategy with the HOTA metric. The project page
is \url{www.mehdimiah.com/mentos+}.Comment: arXiv admin note: text overlap with arXiv:2107.0706
MeNToS : Tracklets association with a space-time memory network
We propose a method for multi-object tracking and segmentation (MOTS) that
does not require fine-tuning or per benchmark hyperparameter selection. The
proposed method addresses particularly the data association problem. Indeed,
the recently introduced HOTA metric, that has a better alignment with the human
visual assessment by evenly balancing detections and associations quality, has
shown that improvements are still needed for data association. After creating
tracklets using instance segmentation and optical flow, the proposed method
relies on a space-time memory network (STM) developed for one-shot video object
segmentation to improve the association of tracklets with temporal gaps. To the
best of our knowledge, our method, named MeNToS, is the first to use the STM
network to track object masks for MOTS. We took the 4th place in the RobMOTS
challenge. The project page is https://mehdimiah.com/mentos.html.Comment: Presented at the "Robust Video Scene Understanding: Tracking and
Video Segmentation" workshop (CVPR-W 2021