ESTANet: Efficient Online Error Detection in Procedural Videos via Prediction Inconsistency
An efficient and accurate system for detecting errors in procedural tasks is crucial for supporting human needs in daily life, as it can provide instant notifications and guide people to correct mistakes. In this work, we study real-time online error detection in procedural videos from a simple but overlooked perspective: the prediction behavior of action detectors themselves. Instead of designing complex architectures or specialized supervision, we observe that action detectors naturally exhibit different prediction characteristics depending on their sensitivity to input dynamics and temporal context. We therefore propose ESTANet (Error-Sensitive and Temporally-vArying Network), a lightw...
| Authors | Shih-Po Lee, Reza Ghoddoosian, Faizan Siddiqui et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
Models and datasets referencing this paper
- shihpolee/ESTANet_datasetsDataset
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