In this work, we introduce a multimodal analysis pipeline that leverages large foundation models in vision and language to analyze corporate social media content, with a focus on sustainability-related communication. Addressing the challenges of evolving, multimodal, and often ambiguous corporate messaging on platforms such as \(\mathbb{X}\) (formerly known as Twitter), we employ an ensemble of large language models (LLMs) to annotate a large corpus of corporate tweets on their topical alignment with the 17 Sustainable Development Goals (SDGs). This approach avoids the need for costly, task-specific annotations and explores the potential of such models as ad hoc annotators for social media data that can efficiently capture both explicit and implicit references to sustainability themes in a scalable manner. Complementing this textual analysis, we utilize vision-language models (VLMs), within a visual understanding framework that uses semantic clusters to uncover patterns in visual sustainability communication. This approach reveals sectoral differences in SDG engagement, temporal trends, and associations between corporate messaging, environmental, social, governance (ESG) risks, and consumer engagement. Our methods, built upon automatic label generation and semantic visual clustering, are broadly applicable to other domains and offer a flexible framework for large-scale social media analysis.

Exquisitor is an interactive multimedia retrieval platform that unites deep learning of visual, textual, and multimodal embeddings with conversational search and relevance feedback, enabling rapid, exploratory navigation of large video collections. Successive competition cycles, most notably the Video Browser Showdown (VBS) and Lifelog Search Challenge (LSC), have driven its evolution, culminating in a redesigned “text-first, feedback-second” workflow and a grouping view that clusters keyframes by video, giving users an immediate, context-rich overview. These changes markedly boosted expert performance, yet a clear proficiency gap persists between experts and novices. The upcoming VR4B session will focus on novices alone, providing a controlled setting to pinpoint the remaining barriers and guide the next round of interface and onboarding refinements.

We introduce multiXview, an interactive retrieval framework for synchronized multi-camera video collections. It features a multi-index search engine that supports natural-language queries over visual embeddings, speech transcripts, and scene descriptions. It supports a synchronized multi-stream player offering parallel playback, and a timeline-based navigation view for temporal scoping and faceted exploration. These components address the redundancy and fragmentation of overlapping egocentric and exocentric video feeds and enable users to locate, aggregate, and reconstruct events across partial perspectives. This paper focuses on system design and implementation, with quantitative and qualitative evaluation to take place at the CASTLE 2025 Grand Challenge Interactive Track.

The Exquisitor search system employs two distinct paradigms for searching media collections: conversational search and user relevance feedback. These paradigms can be used independently or cooperatively within the search process. While we posit that these techniques are complementary, as conversational search can find specific examples for relevance feedback and relevance feedback can help the user better understand the collection and lead to improved queries, empirical user behavior in real-world search scenarios remains poorly understood. To explore how these approaches interact within Exquisitor, we must gather more data about user actions during sessions, as well as their post-session feedback on the system. At IVR4B, we intend to obtain this information through user interaction logs and postsession interviews.

This companion paper is provided to describe the major experiments reported in our paper "On Learning Disentangled Representation for Acoustic Event Detection" published in ACM Multimedia 2019. To make the replication of our work easier, we first give an introduction of the computing environment where all of our experiments are conducted. Furthermore, we provide an environmental configuration file to setup the compiling environment and other artifacts including the source code, datasets and the files generated during our experiments. Finally, we summarize the structure and usage of the source code. For more details, please consult the README file in the archive of artifacts on GitHub: https://github.com/mastergofujs/SED_PyTorch.

In our recent papers, we proposed a new family of residual convolutional neural networks trained for semi-dense and sparse depth reconstruction without use of RGB channel. The proposed models can be used in low-resolution depth sensors or SLAM methods estimating partial depth with certain distributions. We proposed using perceptual loss for training depth reconstruction in order to better preserve edge structure and reduce over-smoothness of models trained on MSE loss alone. This paper contains reproducibility companion guide on training, running and evaluating suggested methods, while also presenting links on further studies in view of reviewers comments and related problems of depth reconstruction.