mlsanityData to 7 Oct 2026

LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks

arXiv 2402.12525 ↗First posted 19 Feb 2024cs.CV cs.AIcited by 12

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Abstract (arXiv)

LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite XAI advancements, an understanding gap persists for end-users with limited domain knowledge in artificial intelligence and computer vision. LangXAI addresses this by furnishing text-based explanations for classification, object detection, and semantic segmentation model outputs to end-users. Preliminary results demonstrate LangXAI's enhanced plausibility, with high BERTScore across tasks, fostering a more transparent and reliable AI framework on vision tasks for end-users.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

27 of 39 references.

References · 27

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15 more
  1. 2020Eigen-CAM: Class Activation Map using Principal Componentscited by 188
  2. 2024GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generationcited by 151
  3. 2023GPT-4V(ision) as a Generalist Evaluator for Vision-Language Taskscited by 118
  4. 2022PromptCap: Prompt-Guided Task-Aware Image Captioningcited by 104
  5. 2020Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networkscited by 79
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  7. 2020TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Linescited by 28
  8. 2023Towards Better Explanations for Object Detectioncited by 14
  9. 2021Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networkscited by 11
  10. 2021EUCA: the End-User-Centered Explainable AI Frameworkcited by 10
  11. 2023Towards Trust of Explainable AI in Thyroid Nodule Diagnosiscited by 10
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  14. 2024XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspectioncited by 1
  15. 2024Transforming gradient-based techniques into interpretable methodscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 2.

12 papers.

  1. 2024XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approachcited by 7
  2. 2025Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretationcited by 6
  3. 2025Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitorscited by 4
  4. 2025Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Modelscited by 4
  5. 2025ODExAI: A Comprehensive Object Detection Explainable AI Evaluationcited by 3
  6. 2025SimInterview: Transforming Business Education through Large Language Model-Based Simulated Multilingual Interview Training Systemcited by 2
  7. 2024Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainabilitycited by 0
  8. 2024A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweetscited by 0
  9. 2025GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Modelscited by 0
  10. 2025A Novel Framework for Automated Explain Vision Model Using Vision-Language Modelscited by 0
  11. 2026Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generationcited by 0
  12. 2026Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Frameworkcited by 0