## 1 Introduction  
Occupational safety and health is a critical process for all industries that require auditing to complement or facilitate the monitoring of events that introduce risk of injury. Information and communication technologies have been at the forefront of business-intelligence tools and services that support safety analysis and effective decision making. Recently, their ability to fully utilize portable devices and their innovative features have provided the means for mitigating technology-oriented design issues and limitations and moving towards a more human-centered approach [1]. Capitalizing on user requirements and skill set, modern tools produce audits and safety reports that collect media-enrichedtemporal and spatial information. This essentially translates to safety data that require more sophisticated maintenance, management, organization and analysis. During the last decade, long-term data has been processed to produce various legislative, regulatory and process-oriented safety recommendations that, in turn, also offer a valuable resource for analysis and classification [2]. Mining incident reports and safety audits is gradually becoming the main paradigm for extracting insights that optimize risk analysis and assessment strategies. Textual databases of safety narratives are being increasingly exploited to aid decision-making tasks and further improve inspection planning, process monitoring and coordination [3]. Being highly confidential and corporate-sensitive, such information silos and data pools have not been shared or open for a long time. Regulatory acts and initiatives have managed to remove a few barriers and achieve public sharing. As a result, research initiatives were able to gain access and analyze reports across many industries, mapping diverse working environments and capturing a wide scope of events [4; 5]. Specifically, text classification is emerging as a key enabler of industrial knowledge management, leveraging the aggregated wealth of corporate and manufacturing information captured in a textual format. Fueled by the ongoing and rapid digital transformation of industry, text mining techniques have been extensively studied for the detection and diagnosis of engineering problems, reliability issues and safety risks. Currently, machine learning is the primary enabler for analytical processes that address challenges that span from labeling and mining reports for the classification of causal factors [6], to developing safety leading indicators and driving predictive policies [7; 8]. The world around us is multimodal. We hear sounds, see images, smell odours and use language to represent high-level concepts. Multimodal processing aims to combine information from multiple sources (modalities) and effectively model inter- and intra-modality interactions. These representations can be used in machine-learning pipelines to solve real-world tasks. Common examples of multimodal tasks involve image captioning [9], visual question answering [10; 11] and sentiment analysis [12; 13; 14]. Furthermore, in multimodal formulations of traditionally unimodal tasks, the inclusion of multiple modalities can significantly improve model performance, e.g., machine translation [15] and speech recognition [16; 17]. In this work, we employ multimodal processing in the field of OSH data analysis. For this, we create a novel dataset, Safety4 All, for the classification of safety-related observations. Safety4 All is a collection of textual and visual observations by expert SOs, with associated metadata, gathered during on-premise safety inspections in real-world businesses. We propose a multimodal architecture that takes advantage of the representational power of modality-specific, large pretrained models for the extraction of unimodal textual and visual representations. These representations are projected in a shared multimodal space through contrastive learning.