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.

LangXAI 是一个框架，将可解释的人工智能 (XAI) 与先进的视觉模型相结合，为视觉识别任务生成文本解释。通过为最终用户提供分类、目标检测和语义分割模型输出的基于文本的解释，LangXAI 填补了人工智能和计算机视觉领域知识有限的最终用户的理解差距。初步结果表明，LangXAI 具有更高的 BERT 分数，增强了其可信度和透明度，为最终用户提供了一个更加透明可靠的视觉任务的人工智能框架。

LangXAI: 整合大型视觉模型生成文本解释以提高视觉感知任务的可解释性

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

In recent years there has been significant progress in time series anomaly
detection. However, after detecting an (perhaps tentative) anomaly, can we
explain it? Such explanations would be useful to triage anomalies. For example,
in an oil refinery, should we respond to an anomaly by dispatching a hydraulic
engineer, or an intern to replace the battery on a sensor? There have been some
parallel efforts to explain anomalies, however many proposed techniques produce
explanations that are indirect, and often seem more complex than the anomaly
they seek to explain. Our review of the literature/checklists/user-manuals used
by frontline practitioners in various domains reveals an interesting
near-universal commonality. Most practitioners discuss, explain and report
anomalies in the following format: The anomaly would be like normal data A, if
not for the corruption B. The reader will appreciate that is a type of
counterfactual explanation. In this work we introduce a domain agnostic
counterfactual explanation technique to produce explanations for time series
anomalies. As we will show, our method can produce both visual and text-based
explanations that are objectively correct, intuitive and in many circumstances,
directly actionable.

近年来，在时间序列异常检测方面取得了显著进展，然而，在检测到异常后，我们能够解释它吗？本文介绍了一种领域无关的反事实解释技术，用于产生时间序列异常的直观且可行动的可视化和基于文本的解释。

PUPAE：直观且可操作的时间序列异常解释

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies

The field of Explainable Artificial Intelligence (XAI) aims to improve the
interpretability of black-box machine learning models. Building a heatmap based
on the importance value of input features is a popular method for explaining
the underlying functions of such models in producing their predictions.
Heatmaps are almost understandable to humans, yet they are not without flaws.
Non-expert users, for example, may not fully understand the logic of heatmaps
(the logic in which relevant pixels to the model's prediction are highlighted
with different intensities or colors). Additionally, objects and regions of the
input image that are relevant to the model prediction are frequently not
entirely differentiated by heatmaps. In this paper, we propose a framework
called TbExplain that employs XAI techniques and a pre-trained object detector
to present text-based explanations of scene classification models. Moreover,
TbExplain incorporates a novel method to correct predictions and textually
explain them based on the statistics of objects in the input image when the
initial prediction is unreliable. To assess the trustworthiness and validity of
the text-based explanations, we conducted a qualitative experiment, and the
findings indicated that these explanations are sufficiently reliable.
Furthermore, our quantitative and qualitative experiments on TbExplain with
scene classification datasets reveal an improvement in classification accuracy
over ResNet variants.

通过采用解释性人工智能技术和预训练的目标检测器，本文提出了一个名为 TbExplain 的框架，用于针对场景分类模型提供基于文本的解释。此外，TbExplain 还结合了一种新的方法，在初始预测不可靠时基于输入图像中的对象统计数据进行预测校正和文本解释。定性实验结果表明，基于文本的解释是相当可靠的。此外，在场景分类数据集上的定量和定性实验结果显示，TbExplain 相较于 ResNet 变种在分类准确性方面取得了改进。