Understanding-Enhanced Model Collaboration for Long-Tailed Egocentric Mistake Detection

• URL: http://arxiv.org/abs/2606.02120v1
• Date: Mon, 01 Jun 2026 11:50:16 GMT
• Title: Understanding-Enhanced Model Collaboration for Long-Tailed Egocentric Mistake Detection
• Abstract summary: We propose an Understanding-Enhanced Model Collaboration Method (UE-MCM)<n>It combines efficient coarse-grained video understanding with accurate fine-grained action reasoning.<n>The resulting system balances speed and accuracy, making it effective for detecting subtle, rare, and ambiguous mistakes in egocentric instructional videos.
• Score: 85.49213290363834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To this end, we propose an Understanding-Enhanced Model Collaboration Method (UE-MCM) that combines efficient coarse-grained video understanding with accurate fine-grained action reasoning. Specifically, UE-MCM contains a small model branch and a large model branch. The large model branch focuses on whether the fine-grained action itself is executed incorrectly, while the small model branch jointly takes the coarse-grained video and fine-grained segment as input to identify actions that may be locally correct but inconsistent with the overall workflow. The small model branch is built on a CLIP4CLIP video encoder initialized from a CLIP model enhanced by Diffusion Contrastive Reconstruction, and the large model branch uses the Qwen3-VL Embedding model to extract high-capacity representations from fine-grained action segments. The small-branch prediction and the large-branch prediction are then adaptively fused by a lightweight collaboration gate. To handle the long-tailed distribution of mistake instances, we optimize the classifiers with complementary objectives, including reweighted cross-entropy, AUC-oriented learning, and label-aware adjustment. The resulting system balances speed and accuracy, making it effective for detecting subtle, rare, and ambiguous mistakes in egocentric instructional videos.

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