PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond

• URL: http://arxiv.org/abs/2409.18260v2
• Date: Thu, 3 Oct 2024 22:57:24 GMT
• Title: PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond
• Authors: Jongseo Lee, Geo Ahn, Seong Tae Kim, Jinwoo Choi,
• Abstract summary: We propose a part contribution evaluation based model explanation (PCEvE) framework. Unlike existing attribution-based XAI approaches, the PCEvE provides a straightforward explanation of a model decision. We rigorously validate the PCEvE via extensive experiments on multiple HFD assessment datasets.
• Score: 5.104096700315427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision are crucial. Existing pixel-level attribution-based explainable AI (XAI) approaches demand considerable effort from users to interpret the semantic information of a region in an image, which can be often time-consuming and impractical. To overcome this challenge, we propose a part contribution evaluation based model explanation (PCEvE) framework. On top of the part detection, we measure the Shapley Value of each individual part to evaluate the contribution to a model decision. Unlike existing attribution-based XAI approaches, the PCEvE provides a straightforward explanation of a model decision, i.e., a part contribution histogram. Furthermore, the PCEvE expands the scope of explanations beyond the conventional sample-level to include class-level and task-level insights, offering a richer, more comprehensive understanding of model behavior. We rigorously validate the PCEvE via extensive experiments on multiple HFD assessment datasets. Also, we sanity-check the proposed method with a set of controlled experiments. Additionally, we demonstrate the versatility and applicability of our method to other domains by applying it to a photo-realistic dataset, the Stanford Cars.

Related papers

• NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization [67.84497768987023]
  NeXT-IMDL is a large-scale diagnostic benchmark designed to probe the boundaries of current detectors.<n>NeXT-IMDL categorizes AIGC-based manipulations along four fundamental axes: editing models, manipulation types, content semantics, and forgery.<n>Our experiments on 11 representative models reveal a critical insight: while these models perform well in their original settings, they exhibit systemic failures and significant performance degradation.
  arXiv  Detail & Related papers  (2025-12-29T11:09:35Z)
• ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models [82.04858317800097]
  We present ForenX, a novel method that not only identifies the authenticity of images but also provides explanations that resonate with human thoughts.<n>ForenX employs the powerful multimodal large language models (MLLMs) to analyze and interpret forensic cues.<n>We introduce ForgReason, a dataset dedicated to descriptions of forgery evidences in AI-generated images.
  arXiv  Detail & Related papers  (2025-08-02T15:21:26Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Decompose the model: Mechanistic interpretability in image models with Generalized Integrated Gradients (GIG) [24.02036048242832]
  This paper introduces a novel approach to trace the entire pathway from input through all intermediate layers to the final output within the whole dataset. We utilize Pointwise Feature Vectors (PFVs) and Effective Receptive Fields (ERFs) to decompose model embeddings into interpretable Concept Vectors. Then, we calculate the relevance between concept vectors with our Generalized Integrated Gradients (GIG) enabling a comprehensive, dataset-wide analysis of model behavior.
  arXiv  Detail & Related papers  (2024-09-03T05:19:35Z)
• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images [49.546627070454456]
  The distribution inconsistency between real and simulated data is the main obstacle that influences the utility of simulated SAR images. We propose a novel trustworthy utility evaluation framework with a counterfactual explanation for simulated SAR images for the first time, denoted as X-Fake. The proposed framework is validated on four simulated SAR image datasets obtained from electromagnetic models and generative artificial intelligence approaches.
  arXiv  Detail & Related papers  (2024-07-28T09:27:53Z)
• CNN-based explanation ensembling for dataset, representation and explanations evaluation [1.1060425537315088]
  We explore the potential of ensembling explanations generated by deep classification models using convolutional model. Through experimentation and analysis, we aim to investigate the implications of combining explanations to uncover a more coherent and reliable patterns of the model's behavior.
  arXiv  Detail & Related papers  (2024-04-16T08:39:29Z)
• Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models [1.3597551064547502]
  In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem. Traditional xAI methods concentrate on visualizing input features that influence model predictions. We present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction.
  arXiv  Detail & Related papers  (2024-04-15T14:26:00Z)
• I-CEE: Tailoring Explanations of Image Classification Models to User Expertise [13.293968260458962]
  We present I-CEE, a framework that provides Image Classification Explanations tailored to User Expertise. I-CEE models the informativeness of the example images to depend on user expertise, resulting in different examples for different users. Experiments with simulated users show that I-CEE improves users' ability to accurately predict the model's decisions.
  arXiv  Detail & Related papers  (2023-12-19T12:26:57Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• Unsupervised discovery of Interpretable Visual Concepts [0.0]
  We propose two methods to explain a model's decision, enhancing global interpretability. One method is inspired by Occlusion and Sensitivity analysis (incorporating causality) The other method uses a novel metric, called Class-aware Order Correlation (CaOC), to globally evaluate the most important image regions.
  arXiv  Detail & Related papers  (2023-08-31T07:53:02Z)
• Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation [64.0476282000118]
  Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality. It is currently difficult to compare the performance of these VCE methods as the evaluation procedures largely vary and often boil down to visual inspection of individual examples and small scale user studies. We propose a framework for systematic, quantitative evaluation of the VCE methods and a minimal set of metrics to be used.
  arXiv  Detail & Related papers  (2023-08-11T12:22:37Z)
• Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos [49.07140708026425]
  Anomaly detection and localization in visual data, including images and videos, are crucial in machine learning and real-world applications.<n>This paper provides the first comprehensive survey focused specifically on explainable 2D visual anomaly detection (X-VAD)<n>We present a literature review of explainable methods, categorized by their underlying techniques.<n>We discuss promising future directions and open problems, including quantifying explanation quality.
  arXiv  Detail & Related papers  (2023-02-13T20:17:41Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)

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.