Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition

Bekhouche, Author2, Author3, Author4, Author5

multimodalTransformeraffective computingfusion methods

2609.19148

Builder Relevance

80%

2h ago

Abstract

This paper presents a novel approach to recognizing ambivalence and hesitancy in clinical videos by detecting cross-modal disagreement using the Modality Discrepancy Transformer.

Reality Card

Core Claim

The Modality Discrepancy Transformer (MDT) outperforms the strongest published baseline by over 10 points in Macro F1 score on the BAH dataset.

Method / Result

Achieved 0.7408 Macro F1 on the labelled test split.

Limitations

The paper does not provide extensive details on the dataset and training conditions, which may affect reproducibility.

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