8^th Multimodal Learning and Applications Workshop

8^th Multimodal Learning and Applications Workshop (MULA 2025)

In recent years, the utilization of big data has greatly advanced Computer Vision and Machine Learning applications. However, the majority of these tasks have focused on only one modality, such as the visual one, with only a few incorporating multiple modalities like audio or thermal. Additionally, the handling of multimodal datasets remains a challenge, particularly in the areas of data acquisition, synchronization, and annotation. As a result, many research investigations have been limited to a single modality, and even when multiple modalities are considered independently, performance tends to suffer when compared to an integrated multimodal learning approach.

Recently, there has been a growing focus on leveraging the synchronization of multimodal streams to enhance the transfer of semantic information. Various works have successfully utilized combinations such as audio/video, RGB/depth, RGB/Lidar, visual/text, text/audio, and more, achieving exceptional outcomes. Additionally, intriguing applications have emerged, employing self-supervised methodologies that enable multiple modalities to learn associations without manual labeling. This approach yields more advanced feature representations as compared to individual modality processing. Moreover, researchers have explored training paradigms that allow neural networks to perform well even when one modality is absent due to sensor failure, impaired functioning, or unfavorable environmental conditions. These topics have garnered significant interest in the computer vision community, particularly in the field of autonomous driving. Furthermore, recent attention has been directed towards the fusion of language (including Large Language models) and vision, such as in the generation of images/videos from text (e.g., DALL-E, text2video), audio (wav2clip), or vice versa (image2speech). Exploiting multimodal scenarios, diffusion models have also emerged as a fascinating framework to explore.

The information fusion from multiple sensors is a topic of major interest also in industry, the exponential growth of companies working on automotive, drone vision, surveillance or robotics are just a few examples. Many companies are trying to automate processes, by using a large variety of control signals from different sources. The aim of this workshop is to generate momentum around this topic of growing interest, and to encourage interdisciplinary interaction and collaboration between computer vision, multimedia, remote sensing, and robotics communities, that will serve as a forum for research groups from academia and industry.

We expect contributions involving, but not limited to, image, video, audio, depth, IR, IMU, laser, text, drawings, synthetic, etc. Position papers with feasibility studies and cross-modality issues with highly applicative flair are also encouraged. Multimodal data analysis is a very important bridge among vision, multimedia, remote sensing, and robotics, therefore we expect a positive response from these communities.

Potential topics include, but are not limited to:

• Multimodal learning
• Cross-modal learning
• Self-supervised learning for multimodal data
• Multimodal data generation and sensors
• Unsupervised learning on multimodal data
• Cross-modal adaptation
• Multimodal data fusion and data representation
• Multimodal transfer learning and Domain Adaptation
• Multimodal scene understanding
• Image and video synthesis by multimodal data
• Multimodal diffusion models 
• LLM in multimodal tasks 
• Vision and Language
• Vision and Sound
• Vision + X
• Multimodal biomedical analysis
• Multimodal applications (e.g. drone vision, autonomous driving, industrial inspection, etc.)
• Fairness and privacy in multimodal learning and applications