Selected Publications
The list here only shows papers for which I am first author and selected publications in which I have participated. Please refer to my Google Scholar Profile for a more exhaustive list.
2026
A Case Study on Large Visual-Language Model Attention Explainability After Adaptation Using Persuasion Strategies in Advertisements
I. Martín-Fernández, M. G. Constantin, B. Ionescu, S. Esteban-Romero, F. Fernández-Martínez, and M. Gil-Martín, “A Case Study on Large Visual-Language Model Attention Explainability After Adaptation Using Persuasion Strategies in Advertisements,” in MultiMedia Modeling, Singapore: Springer Nature, 2026, pp. 103–116. doi: 10.1007/978-981-95-6950-2_8.
@inproceedings{martin-fernandez_case_2026,
title = {A {{Case Study}} on~{{Large Visual-Language Model Attention Explainability After Adaptation Using Persuasion Strategies}} in~{{Advertisements}}},
booktitle = {{{MultiMedia Modeling}}},
author = {{Mart{\'i}n-Fern{\'a}ndez}, Iv{\'a}n and Constantin, Mihai Gabriel and Ionescu, Bogdan and {Esteban-Romero}, Sergio and {Fern{\'a}ndez-Mart{\'i}nez}, Fernando and {Gil-Mart{\'i}n}, Manuel},
editor = {Loko{\v c}, Jakub and Pe{\v s}ka, Ladislav and Zah{\'a}lka, Jan and Rudinac, Stevan and Kastner, Marc and Chen, Jingjing and Hu, Min-Chun and Wu, Jiaxin and Sharma, Ujjwal},
year = 2026,
pages = {103--116},
publisher = {Springer Nature},
address = {Singapore},
doi = {10.1007/978-981-95-6950-2_8},
isbn = {9789819569502},}
A comprehensive study on contrastive pre-training and fine tuning of vision and text transformers for video memorability prediction
I. Martín-Fernández, S. Esteban-Romero, M. Gil-Martín, and F. Fernández-Martínez, “A comprehensive study on contrastive pre-training and fine tuning of vision and text transformers for video memorability prediction,” Multimed Tools Appl, vol. 85, no. 1, p. 30, Jan. 2026, doi: 10.1007/s11042-026-21260-3.
Abstract
Video memorability prediction has emerged as a key challenge for improving information retrieval, content design, and user engagement. Prior work has shown that semantic cues play a crucial role in determining memorability, with recent studies leveraging Contrastive Language-Image Pre-training (CLIP) encoders to incorporate semantic information. However, the specific improvements attributable to CLIP models remain unclear, as few studies systematically compare their performance against equivalent unimodal encoders or explore fine-tuning strategies. This work addresses that gap through a comprehensive, controlled evaluation of CLIP-based and unimodal encoders for video memorability prediction. We propose FCLIP, a domain-adapted extension of CLIP that undergoes additional contrastive pre-training on memorability-specific image-text pairs. Our experiments assess both feature extraction and supervised fine-tuning, ensuring fair comparisons across models with matched architecture and parameter count. Results show that FCLIP image encoders achieve a Spearman Rank Correlation Coefficient (SRCC) of 0.672 on the Memento10k dataset, significantly outperforming unimodal Vision Transformers. FCLIP text encoders similarly outperform unimodal baselines, reaching an SRCC of 0.632. These findings demonstrate that contrastive learning and domain adaptation substantially improve memorability prediction, highlighting the importance of semantic and multimodal pre-training in developing advanced content analysis systems.
@article{martin-fernandez_comprehensive_2026,
title = {A Comprehensive Study on Contrastive Pre-Training and Fine Tuning of Vision and Text Transformers for Video Memorability Prediction},
author = {{Mart{\'i}n-Fern{\'a}ndez}, Iv{\'a}n and {Esteban-Romero}, Sergio and {Gil-Mart{\'i}n}, Manuel and {Fern{\'a}ndez-Mart{\'i}nez}, Fernando},
year = 2026,
month = jan,
journal = {Multimedia Tools and Applications},
volume = {85},
number = {1},
pages = {30},
issn = {1573-7721},
doi = {10.1007/s11042-026-21260-3},
}
Principled Evaluation of Multi-Label Persuasion in Advertisements with Large Vision-Language Models
Iván Martín-Fernández, Mihai Gabriel Constantin, Bogdan Ionescu, Manuel Gil-Martín, and Fernando Fernández-Martínez. 2026. Principled Evaluation of Multi-Label Persuasion in Advertisements with Large Vision-Language Models. ACM Trans. Multimedia Comput. Commun. Appl. Just Accepted (January 2026). https://doi.org/10.1145/3788874
Abstract
The automatic detection of persuasive strategies in advertisements presents a uniquely multimodal challenge at the intersection of vision, language, and social cognition. While recent advances in Large Vision-Language Models (LVLMs) offer promising capabilities for such tasks, current approaches often rely on restrictive evaluation schemes that do not reflect the inherently multi-label nature of persuasive messaging. In this work, we reframe persuasion strategy detection as a genuine multi-label classification problem and propose a principled evaluation framework to enhance interpretability and robustness. We apply this approach to both image and video datasets, examine their characteristics, and introduce novel input-agnostic baselines that achieve macro F1-scores of 0.082 and 0.289 on the respective test sets. As part of this analysis, we study label co-occurrence patterns and dataset ambiguities, providing insights that inform both model interpretation and future dataset design. To assess the native capabilities of LVLMs, we benchmark three open-source models—PaliGemma, PaliGemma2, and Qwen2.5-VL—on the image persuasion dataset. Under zero-shot conditions, we demonstrate that querying each strategy individually with a logit-based decision threshold outperforms guided text generation. The best-performing zero-shot model, Qwen2.5-VL, achieves a macro F1-score of 0.227 and a sample F1-score of 0.234 on the image test set, and 0.381 and 0.400 respectively on the video test set. We further explore and compare two lightweight fine-tuning strategies that update only small subsets of model parameters while keeping the remaining weights frozen: fine-tuning of the image-to-text tokens linear projection and Low Rank Adaptation (LoRA) of the language model. Linear projector fine-tuning yields a top macro F1-score of 0.396 on the image test set, marking a substantial improvement over zero-shot performance. To evaluate cross-modal generalization, we apply fine-tuned image models to the video dataset. Our experiments reveal that, while projection-based fine-tuning enables partial knowledge transfer from image to video (macro F1=0.416 on a testing subset), LoRA adaptation severely disrupts cross-modal performance (macro F1=0.189). Finally, we perform a per-strategy performance analysis, looking into annotator- and data-centric factors that may influence LVLM performance. These findings highlight the viability of open-weight LVLMs for fine-grained persuasion analysis and suggest efficient pathways for domain-specific adaptation under realistic resource constraints.
@article{10.1145/3788874,
author = {Mart\'{\i}n-Fern\'{a}ndez, Iv\'{a}n and Constantin, Mihai Gabriel and Ionescu, Bogdan and Gil-Mart\'{\i}n, Manuel and Fern\'{a}ndez-Mart\'{\i}nez, Fernando},
title = {Principled Evaluation of Multi-Label Persuasion in Advertisements with Large Vision-Language Models},
year = {2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1551-6857},
url = {https://doi.org/10.1145/3788874},
doi = {10.1145/3788874},
note = {Just Accepted},
journal = {ACM Trans. Multimedia Comput. Commun. Appl.},
month = jan,
}
2025
Parameter-Efficient Adaptation of Large Vision—Language Models for Video Memorability Prediction
Iván Martín-Fernández, Sergio Esteban-Romero, Fernando Fernández-Martínez, and Manuel Gil-Martín. 2025. "Parameter-Efficient Adaptation of Large Vision—Language Models for Video Memorability Prediction" Sensors 25, no. 6: 1661. https://doi.org/10.3390/s25061661
Abstract 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.
@article{s25061661,
article-number = {1661},
author = {Mart{\'\i}n-Fern{\'a}ndez, Iv{\'a}n and Esteban-Romero, Sergio and Fern{\'a}ndez-Mart{\'\i}nez, Fernando and Gil-Mart{\'\i}n, Manuel},
doi = {10.3390/s25061661},
issn = {1424-8220},
journal = {Sensors},
number = {6},
pubmedid = {40292713},
title = {Parameter-Efficient Adaptation of Large Vision---Language Models for Video Memorability Prediction},
url = {https://www.mdpi.com/1424-8220/25/6/1661},
volume = {25},
year = {2025},
bdsk-url-1 = {https://www.mdpi.com/1424-8220/25/6/1661},
bdsk-url-2 = {https://doi.org/10.3390/s25061661}
}
2024
Larger Encoders, Smaller Regressors: Exploring Label Dimensionality Reduction and Multimodal Large Language Models as Feature Extractors for Predicting Social Perception
Iván Martín-Fernández, Sergio Esteban-Romero, Jaime Bellver-Soler, Fernando Fernández-Martínez, and Manuel Gil-Martín. 2024. Larger Encoders, Smaller Regressors: Exploring Label Dimensionality Reduction and Multimodal Large Language Models as Feature Extractors for Predicting Social Perception. In Proceedings of the 5th on Multimodal Sentiment Analysis Challenge and Workshop: Social Perception and Humor (MuSe'24). Association for Computing Machinery, New York, NY, USA, 20–27. https://doi.org/10.1145/3689062.3689083
Abstract: 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.
@inproceedings{10.1145/3689062.3689083,
author = {Mart\'{\i}n-Fern\'{a}ndez, Iv\'{a}n and Esteban-Romero, Sergio and Bellver-Soler, Jaime and Fern\'{a}ndez-Mart\'{\i}nez, Fernando and Gil-Mart\'{\i}n, Manuel},
title = {Larger Encoders, Smaller Regressors: Exploring Label Dimensionality Reduction and Multimodal Large Language Models as Feature Extractors for Predicting Social Perception},
year = {2024},
isbn = {9798400711992},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3689062.3689083},
doi = {10.1145/3689062.3689083},
abstract = {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.},
booktitle = {Proceedings of the 5th on Multimodal Sentiment Analysis Challenge and Workshop: Social Perception and Humor},
pages = {20–27},
numpages = {8},
keywords = {affective computing, multimodal large language model., multimodal sentiment analysis, social perception},
location = {Melbourne VIC, Australia},
series = {MuSe'24}
}
2023
Exploring Video Transformers and Automatic Segment Selection for Memorability Prediciton
Martín-Fernández, I., Esteban-Romero, S., Bellver-Soler, J., Gil-Martín, M., & Fernández-Martínez, F. (2023). Exploring Video Transformers and Automatic Segment Selection for Memorability Prediction. Available at https://ceur-ws.org/Vol-3658/paper25.pdf
Abstract: This paper summarises THAU-UPM’s approach and results from the MediaEval 2023 Predicting Video Memorability task. Focused on the generalisation subtask, our work leverages a pre-trained Video Vision Transformer (ViViT), fine-tuned on memorability-related data, to model temporal and spatial relationships in videos. We propose novel, annotator-independent automatic segment selection methods grounded in visual saliency. These methods identify the most relevant video frames prior to conducting memorability score estimation. This selection process is implemented during both training and evaluation phases. Our study demonstrates the effectiveness of fine-tuning the ViViT model compared to a scratchtrained baseline, emphasising the importance of pre-training for predicting memorability. However, the model shows comparable sensitivity to both saliency-based and naive segment selection methods, suggesting that fine-tuning may harness similar benefits from various video segments. These results underscore the robustness of our approach but also signal the need for ongoing research.
@article{martin2023exploring,
title={Exploring Video Transformers and Automatic Segment Selection for Memorability Prediction},
author={Mart{\'\i}n-Fern{\'a}ndez, Iv{\'a}n and Esteban-Romero, Sergio and Bellver-Soler, Jaime and Gil-Mart{\'\i}n, Manuel and Fern{\'a}ndez-Mart{\'\i}nez, Fernando},
year={2023}
}
Video Memorability Prediction From Jointly-learnt Semantic and Visual Features
Iván Martín-Fernández, Ricardo Kleinlein, Cristina Luna-Jiménez, Manuel Gil-Martín, and Fernando Fernández-Martínez. 2023. Video Memorability Prediction From Jointly-learnt Semantic and Visual Features. In Proceedings of the 20th International Conference on Content-based Multimedia Indexing (CBMI '23). Association for Computing Machinery, New York, NY, USA, 178–182. https://doi.org/10.1145/3617233.3617260
Abstract: The memorability of a video is defined as an intrinsic property of its visual features that dictates the fraction of people who recall having watched it on a second viewing within a memory game. Still, unravelling what are the key features to predict memorability remains an obscure matter. This challenge is addressed here by fine-tuning text and image encoders using a cross-modal strategy known as Contrastive Language-Image Pre-training (CLIP). The resulting video-level data representations learned include semantics and topic-descriptive information as observed from both modalities, hence enhancing the predictive power of our algorithms. Our proposal achieves in the text domain a significantly greater Spearman Rank Correlation Coefficient (SRCC) than a default pre-trained text encoder (0.575 ± 0.007 and 0.538 ± 0.007, respectively) over the Memento10K dataset. A similar trend, although less pronounced, can be noticed in the visual domain. We believe these findings signal the potential benefits that cross-modal predictive systems can extract from being fine-tuned to the specific issue of media memorability.
@inproceedings{10.1145/3617233.3617260,
author = {Mart\'{\i}n-Fern\'{a}ndez, Iv\'{a}n and Kleinlein, Ricardo and Luna-Jim\'{e}nez, Cristina and Gil-Mart\'{\i}n, Manuel and Fern\'{a}ndez-Mart\'{\i}nez, Fernando},
title = {Video Memorability Prediction From Jointly-learnt Semantic and Visual Features},
year = {2023},
isbn = {9798400709128},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3617233.3617260},
doi = {10.1145/3617233.3617260},
booktitle = {Proceedings of the 20th International Conference on Content-Based Multimedia Indexing},
pages = {178–182},
numpages = {5},
keywords = {pre-training, media memorability, cross-modal, CLIP},
location = {<conf-loc>, <city>Orleans</city>, <country>France</country>, </conf-loc>},
series = {CBMI '23}
}