Cross-modal retrieval, as an important emerging foundational information retrieval task, benefits from recent advances in multimodal technologies. However, current cross-modal retrieval methods mainly focus on the interaction between textual information and 2D images, lacking research on 3D data, especially point clouds at scene level, despite the increasing role point clouds play in daily life. Therefore, in this paper, we proposed a cross-modal point cloud retrieval benchmark that focuses on using text or images to retrieve point clouds of indoor scenes. Given the high cost of obtaining point cloud compared to text and images, we first designed a pipeline to automatically generate a large number of indoor scenes and their corresponding scene graphs. Based on this pipeline, we collected a balanced dataset called CRISP, which contains 10K point cloud scenes along with their corresponding scene images and descriptions. We then used state-of-the-art models to design baseline methods on CRISP. Our experiments demonstrated that point cloud retrieval accuracy is much lower than cross-modal retrieval of 2D images, especially for textual queries. Furthermore, we proposed ModalBlender, a tri-modal framework which can greatly improve the Text-PointCloud retrieval performance. Through extensive experiments, CRISP proved to be a valuable dataset and worth researching. (Dataset can be downloaded at https://github.com/CRISPdataset/CRISP.)

Textbook question answering is challenging as it aims to automatically answer various questions on textbook lessons with long text and complex diagrams, requiring reasoning across modalities. In this work, we propose MRHF, a novel framework that incorporates dense passage re-ranking and the mixture-of-experts architecture for TQA. MRHF proposes a novel query augmentation method for diagram questions and then adopts multi-stage dense passage re-ranking with large pretrained retrievers for retrieving paragraph-level contexts. Then it employs a unified question solver to process different types of text questions. Considering the rich blobs and relation knowledge contained in diagrams, we propose to perform multimodal feature fusion over the retrieved context and the heterogeneous diagram features. Furthermore, we introduce the mixture-of-experts architecture to solve the diagram questions to learn from both the rich text context and the complex diagrams and mitigate the possible negative effects between features of the two modalities. We test the framework on the CK12-TQA benchmark dataset, and the results show that MRHF outperforms the state-of-the-art results in all types of questions. The ablation and case study also demonstrates the effectiveness of each component of the framework.