We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question- and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa.

Social media plays a significant role for sports organizations with millions of active fans, but publishing highlights is often a tedious manual operation. With the development of AI, new tools are available for content generation and personalization to engage audiences. We propose an AI-based multimedia production framework for the automatic publishing of soccer and ice hockey highlights on social media, and disseminate our experiences with developing dedicated pipelines for event detection and classification, player detection and tracking, highlight clipping, cropping, thumbnail generation, game summarization, caption generation, and social media sharing.

The rapid advancement of technology has been revolutionizing the field of sports media, where there is a growing need for sophisticated data processing methods. Current methodologies for extracting information from soccer broadcast videos to generate game highlights and summaries for social media are predominantly manual and rely heavily on text-based NLP techniques, overlooking the rich visual and auditory information available. In response to this challenge, our research introduces SoccerSum, a tool that innovates in the field by integrating computer vision, audio analysis with advanced language models like GPT-4. This multimodal approach enables automated, enriched content summarization, including detection of players and key field elements, thereby enhancing the metadata used in summarization algorithms. SoccerSum uniquely combines textual and visual data, offering a comprehensive solution for generating accurate, platform-specific content. This development represents a significant advancement in automated, data-driven sports media dissemination, and sets a new benchmark in the realm of soccer information extraction. A video of the demo can be found here: https://youtu.be/za4VIi2ARXY.

With the increasing availability of multimodal data, especially in the sports and medical domains, there is growing interest in developing Artificial Intelligence (AI) models capable of comprehending the world in a more holistic manner. Nevertheless, various challenges exist in multimodal understanding, including the integration of multiple modalities and the resolution of semantic gaps between them. The proposed research aims to leverage multiple input modalities for the multimodal understanding of AI models, enhancing their reasoning, generation, and intelligent behavior. The research objectives focus on developing novel methods for multimodal AI, integrating them into conversational agents with optimizations for domain-specific requirements. The research methodology encompasses literature review, data curation, model development and implementation, evaluation and performance analysis, domain-specific applications, and documentation and reporting. Ethical considerations will be thoroughly addressed, and a comprehensive research plan is outlined to provide guidance. The research contributes to the field of multimodal AI understanding and the advancement of sophisticated AI systems by experimenting with multimodal data to enhance the performance of state-of-the-art neural networks.

The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. In this paper, we introduce SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems in terms of accuracy and user engagement. The results underscore the potential of using RAG and LLMs in sports analytics, paving the way for future advancements in the accessibility and real-time processing of sports data.