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