The Multimodal Sentiment Analysis Challenge presents two distinct sub-challenges related to human perception characteristics. This paper focuses on the MUSE-PERCEPTION challenge, which aims to predict the perceptual attributes of CEOs from video data. Specifically, we propose a novel approach that leverages Multimodal Large Language Models (MM-LLM) to integrate multimodal features and generate accurate predictions. Our proposal explores both the direct generation of target values using tuned Qwen-VL and the extraction of semantic representations from a pre-trained Gemma-2B model. The latter integrates multimodal data such as facial expressions (vit-fer), audio (w2v-msp) and additional features such as egemaps or facenet512 results. The input data involve a task-specific cue and an audio transcript. Feature projectors map each modality into the semantic space of the LLM, and mean or attention clustering aggregates the final hidden state of the LLM. Next, an MLP uses these aggregated representations to predict 16 perceptual attributes. To optimize model performance, we experimented with several fitting methods and implemented a combined mean absolute error (MAE) and Pearson correlation coefficient (ρ) loss function. The experimental results shows the effectiveness of this approach, and the Gemma-2B-based model achieves a solid second place in the challenge. These results underscore the potential of MM-LLMs to improve the understanding of human perception in professional contexts.

Designing reliable automatic models for social perception can contribute to a better understanding of human behavior, enabling more trustworthy experiences in the multimedia on-line communication environment. However, predicting social attributes from video data remains challenging due to the complex interplay of visual, auditory, and linguistic cues. In this paper, we address this challenge by investigating the effectiveness of Multimodal Large Language Models (MM-LLMs) for feature extraction in the MuSe-Perception challenge. Firstly, our analysis of the novel LMU-ELP dataset has revealed high correlations between certain perceptual dimensions, motivating using a single regression model for all 16 social attributes to be predicted for a set of speakers appearing in recorded video clips. We demonstrate that dimensionality reduction through Principal Component Analysis (PCA) can be applied to the label space without a relevant performance loss. Secondly, by employing frozen MM-LLMs as feature extractors, we explore their ability to capture perception-related information. We extract sequence embeddings from the Qwen-VL and Qwen-Audio models and train a Multi-Layer Perceptron over the attention-pooled vectors for each one of the encoders, obtaining a mean Pearson correlation of 0.22 using the average predictions for both models. Our best result of 0.31 is achieved by training the same architecture over the baseline vit-ver and w2v-msp features, which motivates further exploration on how to effectively leverage advanced MM-LLMs as feature extractors. Lastly, a post hoc analysis of our results highlights the limitations of Pearson correlation for evaluating regression performance in this context. In particular, a similar Pearson coefficient can be obtained with two very different prediction sets displaying different levels of variability. We take this result as a call to action in exploring alternative metrics to assess the regression performance for the task.

The accurate modelling of video memorability, or the intrinsic properties that render a piece of audiovisual content more likely to be remembered, will facilitate the development of automatic systems that are more efficient in retrieving, classifying and generating impactful media. Recent studies have indicated a strong correlation between the visual semantics of video and its memorability. This underscores the importance of developing advanced visual comprehension abilities to enhance model performance. It has been demonstrated that Large Vision–Language Models (LVLMs) demonstrate exceptional proficiency in generalist, high-level semantic comprehension of images and video, due to their extensive multimodal pre-training on a vast scale. This work makes use of the vast generalist knowledge of LVLMs and explores efficient adaptation techniques with a view to utilising them as memorability predictors. In particular, the Quantized Low-Rank Adaptation (QLoRA) technique is employed to fine-tune the Qwen-VL model with memorability-related data extracted from the Memento10k dataset. In light of existing research, we propose a particular methodology that transforms Qwen-VL from a language model to a memorability score regressor. Furthermore, we consider the influence of selecting appropriate LoRA hyperparameters, a design aspect that has been insufficiently studied. We validate the LoRA rank and alpha hyperparameters using 5-Fold Cross-Validation and evaluate our best configuration on the official testing portion of the Memento10k dataset, obtaining a state-of-the-art Spearman Rank Correlation Coefficient (SRCC) of 0.744. Consequently, this work represents a significant advancement in modelling video memorability through high-level semantic understanding.

Olfactory information, crucial for human perception, is often underrepresented compared to visual and textual data. This work explores methods for understanding smell descriptions within a multimodal context, where scent information is conveyed indirectly through text and images. We address the challenges of the Multimodal Understanding of Smells in Texts and Images (MUSTI) task by proposing novel approaches that leverage language-specific models and state-of-the-art multimodal large language models (MM-LLMs). Our core contribution is a multimodal framework using language-specific encoders for text and image data. This allows for a joint embedding space that explores the semantic symmetry between smells, texts, and images to identify olfactory-related connections shared across the modalities. While ensemble learning with language-specific models achieved good performance, MM-LLMs demonstrated exceptional potential. Fine-tuning a quantized version of the Qwen-VL-Chat model achieved a state-of-the-art macro F1-score of 0.7618 on the MUSTI task. This highlights the effectiveness of MM-LLMs in capturing task requirements and adapting to specific formats.

Human gesture recognition using electromyography (EMG) signals holds high potential for enhancing the functionality of human–machine interfaces, prosthetic devices, and sports performance analysis. This work proposes a gesture classification system based on electromyography. This system has been designed to improve the accuracy of forearm gesture classification by leveraging advanced signal processing and deep learning techniques to optimize classification accuracy. The system is composed of two main modules: a signal processing module, able to perform two main transforms (short-time Fourier transform and Constant-Q-Transform), and a classification module based on convolutional neural networks (CNNs). The dataset employed in this study, entitled “Latent Factors Limiting the Performance of sEMG-Interfaces”, comprises EMG signals collected via a bracelet equipped with 8 distinct sensors, capable of capturing a wide range of forearm muscle activities. The experimental process is composed of two main phases. Firstly, we employed a k-fold cross-validation methodology to systematically assess and validate the model’s performance across different subsets of data for hyperparameter tunning. Secondly, the best system configuration was evaluated using a new subset, reporting significant improvements. The baseline neural network architecture reported an accuracy of 85.0 ± 0.13% when classifying gestures. Through rigorous hyperparameter tuning and the application of various mathematical transformations to the EMG features, we managed to enhance the classification accuracy to 90.0 ± 0.12% (an absolute improvement of 5% compared to the baseline for a 5-class problem). When making comparisons to previous works, we significantly improved the F-score from 85.5% to 89.3% for a 4-class problem (left, right, up, and down).