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Mistake Detection

Mistakes are natural occurrences in many tasks and an opportunity for an AR assistant to provide help. Identifying such mistakes requires modelling procedural knowledge and retaining long-range sequence information. In its simplest form Mistake Detection aims to classify each coarse action segment into one of the three classes: {“correct”, “mistake”, “correction”}.

Papers

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Showing 1–14 of 14 papers

TitleStatusHype
AUTOMATIC PRONUNCIATION MISTAKE DETECTOR PROJECT REPORT—0
Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts—0
Task Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric VideosCode1
Explainable Procedural Mistake Detection—0
TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric VideosCode1
EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos with Procedural Texts—0
Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning—0
Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities—0
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric VideosCode1
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought—0
PREGO: online mistake detection in PRocedural EGOcentric videosCode1
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World—0
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural ActivitiesCode0
Lexical Normalization of User-Generated Medical Text—0
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