Bounding boxes are often used to communicate automatic object detection
results to humans, aiding humans in a multitude of tasks. We investigate the
relationship between bounding box localization errors and human task
performance. We use observer performance studies on a visual multi-object
counting task to measure both human trust and performance with different levels
of bounding box accuracy. The results show that localization errors have no
significant impact on human accuracy or trust in the system. Recall and
precision errors impact both human performance and trust, suggesting that
optimizing algorithms based on the F1 score is more beneficial in
human-computer tasks. Lastly, the paper offers an improvement on bounding boxes
in multi-object counting tasks with center dots, showing improved performance
and better resilience to localization inaccuracy.

通过观察多目标计数任务中观察者表现研究，探究了边界框定位误差与人类任务表现之间的关系，并发现定位误差对人类准确性和信任度没有显著影响，而召回率和精确率误差则会影响人类的表现和信任度，因此优化算法时基于 F1 分数更有利于人机任务。最后，本文提出了在带有中心点的多目标计数任务中的边界框改进方法，展示了改进的性能和更好的对定位误差的韧性。

目标检测定位错误是否影响人类表现和信任度？

Do Object Detection Localization Errors Affect Human Performance and  Trust?

Given video demonstrations and paired narrations of an at-home procedural
task such as changing a tire, we present an approach to extract the underlying
task structure -- relevant actions and their temporal dependencies -- via
action-centric task graphs. Learnt structured representations from our method,
Action Dynamics Task Graphs (ADTG), can then be used for understanding such
tasks in unseen videos of humans performing them. Furthermore, ADTG can enable
providing user-centric guidance to humans in these tasks, either for performing
them better or for learning new tasks. Specifically, we show how ADTG can be
used for: (1) tracking an ongoing task, (2) recommending next actions, and (3)
planning a sequence of actions to accomplish a procedural task. We compare
against state-of-the-art Neural Task Graph method and demonstrate substantial
gains on 18 procedural tasks from the CrossTask dataset, including 30.1%
improvement in task tracking accuracy and 20.3% accuracy gain in next action
prediction.

本文通过 Action Dynamics Task Graphs (ATDG) 提取任务的结构 - 相关动作及其时间依赖性来理解人类执行任务的未见视频。ATDG 还可以用于提供用户中心指导，包括跟踪正在执行中的任务、推荐下一步行动和规划动作序列，相较于当前最新的神经任务图法，在 CrossTask 数据集的 18 项操作中，改进了 30.1％的任务跟踪准确性和 20.3％的下一步行动预测准确度。

学习可规划程序任务的表示的动作动态任务图

Action Dynamics Task Graphs for Learning Plannable Representations of Procedural Tasks

Machine learning (ML) systems across many application areas are increasingly
demonstrating performance that is beyond that of humans. In response to the
proliferation of such models, the field of Explainable AI (XAI) has sought to
develop techniques that enhance the transparency and interpretability of
machine learning methods. In this work, we consider a question not previously
explored within the XAI and ML communities: Given a computational system whose
performance exceeds that of its human user, can explainable AI capabilities be
leveraged to improve the performance of the human? We study this question in
the context of the game of Chess, for which computational game engines that
surpass the performance of the average player are widely available. We
introduce the Rationale-Generating Algorithm, an automated technique for
generating rationales for utility-based computational methods, which we
evaluate with a multi-day user study against two baselines. The results show
that our approach produces rationales that lead to statistically significant
improvement in human task performance, demonstrating that rationales
automatically generated from an AI's internal task model can be used not only
to explain what the system is doing, but also to instruct the user and
ultimately improve their task performance.

研究了如何设计一个可以帮助人类玩家改善棋类游戏表现的算法，该算法利用机器学习及透明度技术生成 AI 的内部任务模型解释，提供了自动判定和指导用户的决策方法。