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.

Chinese herbal medicine is an important part of traditional Chinese medicine (TCM). With developing of traditional Chinese medicine, the usage of Chinese herbal medicine is growing rapidly. It is essential to identify Chinese herbal medicine correctly since Chinese herbal medicine is used to treat disease. However, identifying Chinese herbal medicine is a hard task because lots of Chinese herbal medicine with different properties displays similar appearance, such as Radix StephaniaeTetrandrae and Radix Paeoniae Alba. Traditional methods of classifying Chinese herbal medicine are low-efficiency and rely on professional medical knowledge. Machine learning methods can reduce the need for professional knowledge in some fields due to its self-learning ability. In this study, a framework, called CNN & BLS, combining the convolutional neural network (CNN) with broad learning system (BLS) for identifying the Chinese herbal medicine, is proposed. Experimental results show the CNN & BLS displays the promising performance for identifying Chinese herbal medicine.

The floating centroids method (FCM) effectively enhances the performance of neural network classifiers. However, the problem of optimizing the neural network continues to restrict the further improvement of FCM. Traditional particle swarm optimization algorithm (PSO) sometimes converges to a local optimal solution in multimodal landscape, particularly for optimizing neural networks. Therefore, the dynamic multilayer PSO (DMLPSO) is proposed to optimize the neural network for improving the performance of FCM. DMLPSO adopts the basic concepts of multi-layer PSO to introduce a dynamic reorganizing strategy, which achieves that valuable information dynamically interacts among different subswarms. This strategy increases population diversity to promote the performance of DMLPSO when optimizing multimodal functions. Experimental results indicate that the proposed DMLPSO enables FCM to obtain improved solutions in many data sets.