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The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the * Both authors contributed equally to this research.

As deep learning research continues to advance, interpretability is becoming as important as model performance. Conducting interpretability studies to understand the decision-making processes of deep learning models can improve performance and provide valuable insights for humans. The interpretability of visual question answering (VQA), a crucial task for human–computer interaction, has garnered the attention of researchers due to its wide range of applications. The generation of natural language explanations for VQA that humans can better understand has gradually supplanted heatmap representations as the mainstream focus in the field. Humans typically answer questions by first identifying the primary objects in an image and then referring to various information sources, both within and beyond the image, including prior knowledge. However, previous studies have only considered input images, resulting in insufficient information that can lead to incorrect answers and implausible explanations. To address this issue, we introduce multiple references in addition to the input image. Specifically, we propose a multimodal model that generates natural language explanations for VQA. We introduce outside knowledge using the input image and question and incorporate object information into the model through an object detection module. By increasing the information available during the model generation process, we significantly improve VQA accuracy and the reliability of the generated explanations. Moreover, we employ a simple and effective feature fusion joint vector to combine information from multiple modalities while maximizing information preservation. Qualitative and quantitative evaluation experiments demonstrate that the proposed method can generate more reliable explanations than state-of-the-art methods while maintaining answering accuracy.

Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient’s conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use a new metric named similarity to accurately evaluate the proposed model’s performance. The experimental results on the Slake and VQA-RAD datasets demonstrate that the proposed method can generate questions with diverse information. Our model works with an answering model for interactive automated clinical diagnosis and generates datasets to replace the process of annotation that incurs huge labor costs.