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Experiments on a new approach for fine-grained evaluation demonstrate that this approach enhances a model’s ability to understand fine-grained differences.
This work aims to establish a unified perspective on vision-based mistake analysis in procedural activities, highlighting its potential across diverse domains and aspects and categorizing approaches based on their use of procedural structure, supervision levels and learning strategies.
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