VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis

• URL: http://arxiv.org/abs/2505.03132v1
• Date: Tue, 06 May 2025 03:09:15 GMT
• Title: VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
• Authors: Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono, Jiajing Guo, Vikram Mohanty, Shekar Arvind Kumar, Liang Gou, Bei Wang, Liu Ren,
• Abstract summary: We introduce VISLIX, a novel visual analytics framework to help domain experts analyze slices in computer vision models.<n>Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively.
• Score: 14.4831165224805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor-intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human-in-the-loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state-of-the-art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models.

Related papers

• A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning [67.72413262980272]
  Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear.<n>We develop SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck.<n>Our approach achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations.
  arXiv  Detail & Related papers  (2025-03-10T06:18:31Z)
• PUB: Plot Understanding Benchmark and Dataset for Evaluating Large Language Models on Synthetic Visual Data Interpretation [2.1184929769291294]
  This paper presents a novel synthetic dataset designed to evaluate the proficiency of large language models in interpreting data visualizations. Our dataset is generated using controlled parameters to ensure comprehensive coverage of potential real-world scenarios. We employ multimodal text prompts with questions related to visual data in images to benchmark several state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-04T11:19:17Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation [57.40024206484446]
  We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models. BVS supports a large number of adjustable parameters at the scene level. We showcase three example application scenarios.
  arXiv  Detail & Related papers  (2024-05-15T17:57:56Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding [29.07617945233152]
  Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance.<n>This approach faces significant challenges, including the laborious and costly requirement for additional metadata.<n>We introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for metadata-free data slice finding.<n>Our system identifies interpretable data slices that involve common model behaviors and visualizes these patterns through an Attribution Mosaic design.
  arXiv  Detail & Related papers  (2024-01-12T09:17:32Z)
• SeeBel: Seeing is Believing [0.9790236766474201]
  We propose three visualizations that enable users to compare dataset statistics and AI performance for segmenting all images. Our project tries to further increase the interpretability of the trained AI model for segmentation by visualizing its image attention weights. We propose to conduct surveys on real users to study the efficacy of our visualization tool in computer vision and AI domain.
  arXiv  Detail & Related papers  (2023-12-18T05:11:00Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation [9.996986104171754]
  We introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debug and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations.
  arXiv  Detail & Related papers  (2021-09-12T22:52:12Z)
• Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation [0.5161531917413706]
  We develop Data Augmentation from Proficient Pre-Training of Robust Generative Adrial Networks (DAPPER GAN) DAPPER GAN is an ML analytics support tool that automatically generates novel views of training images. We experimentally evaluate this technique on the Stanford Cars dataset, demonstrating improved vehicle make and model classification accuracy.
  arXiv  Detail & Related papers  (2020-08-12T15:29:11Z)

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.