A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024

• URL: http://arxiv.org/abs/2410.17963v1
• Date: Wed, 23 Oct 2024 15:30:37 GMT
• Title: A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024
• Authors: Horacio Thompson, Marcelo Errecalde,
• Abstract summary: The eRisk laboratory aims to address issues related to early risk detection on the Web. Our research group solved Task 2 by defining a CPI+DMC approach, addressing both objectives independently, and a time-aware approach. We achieved outstanding results for the ERDE50 metric and ranking-based metrics, demonstrating consistency in solving ERD problems.
• Score: 0.9208007322096532
• License:
• Abstract: The eRisk laboratory aims to address issues related to early risk detection on the Web. In this year's edition, three tasks were proposed, where Task 2 was about early detection of signs of anorexia. Early risk detection is a problem where precision and speed are two crucial objectives. Our research group solved Task 2 by defining a CPI+DMC approach, addressing both objectives independently, and a time-aware approach, where precision and speed are considered a combined single-objective. We implemented the last approach by explicitly integrating time during the learning process, considering the ERDE{\theta} metric as the training objective. It also allowed us to incorporate temporal metrics to validate and select the optimal models. We achieved outstanding results for the ERDE50 metric and ranking-based metrics, demonstrating consistency in solving ERD problems.

Related papers

• Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement [28.370473108391426]
  This paper briefly introduces the solutions developed by our team, HFUT-VUT, for Track 1 of self-supervised heart rate measurement. The goal is to develop a self-supervised Physiological for heart rate (HR) using unlabeled facial videos. Our solutions achieved a remarkable RMSE score of 8.85277 on the test dataset, securing bftext2nd place in Track 1 of the challenge.
  arXiv  Detail & Related papers  (2024-06-07T13:53:02Z)
• Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task. We name our approach Adaptive Retention & Correction (ARC) ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023 [1.0878040851637998]
  MentalRiskES is a novel challenge that proposes to solve problems related to early risk detection for the Spanish language. The objective is to detect, as soon as possible, Telegram users who show signs of mental disorders considering different tasks.
  arXiv  Detail & Related papers  (2023-10-30T20:38:31Z)
• Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023 [0.9208007322096532]
  The CLEF eRisk Laboratory explores solutions to different tasks related to risk detection on the Internet. In the 2023 edition, Task 1 consisted of searching for symptoms of depression, the objective of which was to extract user writings according to their relevance to the BDI Questionnaire symptoms. Task 2 was related to the problem of early detection of pathological gambling risks, where the participants had to detect users at risk as quickly as possible. Task 3 consisted of estimating the severity levels of signs of eating disorders.
  arXiv  Detail & Related papers  (2023-10-30T19:34:33Z)
• EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning [26.535329379980094]
  EvCenterNet is a novel uncertainty-aware 2D object detection framework. We employ evidential learning to estimate both classification and regression uncertainties. We train our model on the KITTI dataset and evaluate it on challenging out-of-distribution datasets.
  arXiv  Detail & Related papers  (2023-03-06T11:07:11Z)
• ReAct: Temporal Action Detection with Relational Queries [84.76646044604055]
  This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries. We first propose a relational attention mechanism in the decoder, which guides the attention among queries based on their relations. Lastly, we propose to predict the localization quality of each action query at inference in order to distinguish high-quality queries.
  arXiv  Detail & Related papers  (2022-07-14T17:46:37Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Sequential Transfer in Reinforcement Learning with a Generative Model [48.40219742217783]
  We show how to reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. We derive PAC bounds on its sample complexity which clearly demonstrate the benefits of using this kind of prior knowledge. We empirically verify our theoretical findings in simple simulated domains.
  arXiv  Detail & Related papers  (2020-07-01T19:53:35Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)
• Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error [50.83356836818667]
  We develop a novel meta-regularization objective using it cyclical annealing schedule and it maximum mean discrepancy (MMD) criterion. The experimental results show that our approach substantially outperforms standard meta-learning algorithms.
  arXiv  Detail & Related papers  (2020-03-04T04:43:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.