The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous image retrieval methods faces several challenges, such as the lack of global feature representation and inadequate text retrieval ability for complex driving scenes. To address these issues, firstly, we propose the BEV-TSR framework which leverages descriptive text as an input to retrieve corresponding scenes in the Bird’s Eye View (BEV) space. Then to facilitate complex scene retrieval with extensive text descriptions, we employ a large language model (LLM) to extract the semantic features of the text inputs and incorporate knowledge graph embeddings to enhance the semantic richness of the language embedding. To achieve feature alignment between the BEV feature and language embedding, we propose Shared Cross-modal Embedding with a set of shared learnable embeddings to bridge the gap between these two modalities, and employ a caption generation task to further enhance the alignment. Furthermore, there lack of well-formed retrieval datasets for effective evaluation. To this end, we establish a multi-level retrieval dataset, nuScenes-Retrieval, based on the widely adopted nuScenes dataset. Experimental results on the multi-level nuScenes-Retrieval show that BEV-TSR achieves state-of-the-art performance, e.g., 85.78% and 87.66% top-1 accuracy on scene-to-test and text-to-scene retrieval respectively.

Recently deep learning has been applied to decompose table structure with the main ideas of detecting table lines and then forming table cells. However, the existing methods face problems in dealing with tables with rotation or no internal table lines. To tackle these problems, we propose a novel table structure decomposition method, which directly detects table cells as objects and creates table structure. Extensions to the existing object detection models including effective table projection module are proposed to adapt to the table cell detection. To support the training of the enhanced models, we create a large image-based table dataset TableCell with cell level annotations. A novel and efficient semi-supervised method is proposed to annotate this new dataset. Experiments demonstrate that our proposed table structure decomposition method is simple, effective and robust to the tables without table lines or with rotation. Our dataset and code will be made available11https://github.com/weidafeng/TableCell.