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Mistake Detection
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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”}.
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Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts
Task Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric Videos
Explainable Procedural Mistake Detection
TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos
EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos with Procedural Texts
Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
PREGO: online mistake detection in PRocedural EGOcentric videos
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.