The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples.

The use of Shapley Values (SVs) to explain machine learning model predictions is established. Recent research efforts have been devoted to generating efficient Neural Network-based SVs estimates. However, the variability of the generated estimates, which depend on the selected data sampling, model, and training parameters, brings the reliability of such estimates into question. By leveraging the concept of Interval SVs, we propose to incorporate SVs uncertainty directly into the learning process. Specifically, we explain ensemble models composed of multiple predictors, each one generating potentially different outcomes. Unlike all existing approaches, the explainer design is tailored to Interval SVs learning instead of SVs only. We present three new Network-based explainers relying on different ISV paradigms, i.e., a Multi-Task Learning network inspired by the Shapley value's weighted least squares characterization and two Interval Shapley-Like Value Neural estimators. The experiments thoroughly evaluate the new approaches on ten benchmark datasets, looking for the best compromise between intervals' accuracy and explainers' efficiency.

Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, Luca Cagliero. Findings of the Association for Computational Linguistics ACL 2024. 2024.