From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition

• URL: http://arxiv.org/abs/2402.12790v1
• Date: Tue, 20 Feb 2024 07:58:04 GMT
• Title: From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition
• Authors: Kimji N. Pellano, Inga Str\"umke, Espen Alexander F. Ihlen
• Abstract summary: This paper tackles the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain. We have tested established XAI metrics namely faithfulness and stability on Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) Our findings indicate that textitfaithfulness may not be a reliable metric in certain contexts, such as with the EfficientGCN model.
• Score: 0.16385815610837165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction. This paper tackles a well-known gap in the field, which is the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain. We have tested established XAI metrics namely faithfulness and stability on Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) to address this problem. The study also introduces a perturbation method that respects human biomechanical constraints to ensure realistic variations in human movement. Our findings indicate that \textit{faithfulness} may not be a reliable metric in certain contexts, such as with the EfficientGCN model. Conversely, stability emerges as a more dependable metric when there is slight input data perturbations. CAM and Grad-CAM are also found to produce almost identical explanations, leading to very similar XAI metric performance. This calls for the need for more diversified metrics and new XAI methods applied in skeleton-based HAR.

Related papers

• Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics [10.045644410833402]
  We introduce LATEC, a large-scale benchmark that critically evaluates 17 prominent XAI methods using 20 distinct metrics. We showcase the high risk of conflicting metrics leading to unreliable rankings and consequently propose a more robust evaluation scheme. LATEC reinforces its role in future XAI research by publicly releasing all 326k saliency maps and 378k metric scores as a (meta-evaluation) dataset.
  arXiv  Detail & Related papers  (2024-09-25T09:07:46Z)
• Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy [0.13194391758295113]
  Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements.
  arXiv  Detail & Related papers  (2024-08-14T00:27:09Z)
• Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning [8.012146883983227]
  Metric learning plays a critical role in training image retrieval and classification. Hyperbolic embedding can be more effective in representing the hierarchical data structure. We propose two types of uncertainty-aware metric learning, for the popular Contrastive learning and conventional margin-based metric learning.
  arXiv  Detail & Related papers  (2023-10-12T15:00:06Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• An Experimental Investigation into the Evaluation of Explainability Methods [60.54170260771932]
  This work compares 14 different metrics when applied to nine state-of-the-art XAI methods and three dummy methods (e.g., random saliency maps) used as references. Experimental results show which of these metrics produces highly correlated results, indicating potential redundancy.
  arXiv  Detail & Related papers  (2023-05-25T08:07:07Z)
• BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence [1.2948254191169823]
  We show how varying perturbations can help to avoid potentially flawed conclusions. We also show how treatment of categorical variables is an important consideration in both post-hoc explainability and ablation studies.
  arXiv  Detail & Related papers  (2022-07-12T14:38:37Z)
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels [138.41576366096137]
  We propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, textiti.e., class-wise divergence and sample-wise consistency. Our method achieves state-of-the-art performance compared with current deep metric learning approaches.
  arXiv  Detail & Related papers  (2021-10-29T02:12:18Z)
• A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI [0.0]
  XAI aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative means. The ideology behind trusting a machine to tend towards the livelihood of a human poses an ethical conundrum. XAI methods produce visualization of the feature contribution towards a given models output on both a local and global level.
  arXiv  Detail & Related papers  (2021-03-08T18:15:52Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• GO FIGURE: A Meta Evaluation of Factuality in Summarization [131.1087461486504]
  We introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. Our benchmark analysis on ten factuality metrics reveals that our framework provides a robust and efficient evaluation. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.
  arXiv  Detail & Related papers  (2020-10-24T08:30:20Z)

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.