3D semantic segmentation remains a pivotal challenge for autonomous driving due to the inherent sparsity of points. Existing CNN-based and Transformer-based methods struggle with either limited receptive fields or quadratic computational complexity. Although some Mamba-based 3D models are designed efficiently with linear complexity, they often overlook the long-term decay problem in Selective State-space Models when processing extremely long sequences in large-scale scenes. In this paper, we propose a Hierarchical Bi-directional Mamba (HB-Mamba) for point cloud semantic segmentation. By decoupling feature extraction into a Global Memory branch and a Local Detail branch, our architecture effectively captures long-range semantics and preserves fine-grained geometric information. Besides, we further introduce a Spatial-Channel Fusion Block to dynamically fuse these multi-scale representations. Experimental results on the nuScenes-Lidarseg benchmark demonstrate that HB-Mamba achieves state-of-the-art performance among Lidar-only methods, reaching 82.8% mIoU on the test set and 81.33% mIoU on the validation set, outperforming the leading transformer-based model PTv3 by 0.1% and 1.01%, respectively.

Text-to-SQL technology converts natural language queries into SQL statements for database retrieval. Recent advances in large language models (LLMs) have improved Text-to-SQL performance, but generated SQL often contains semantic or syntax errors that degrade user experience and system stability. Existing SQL error detection methods are costly, lack interpretability, and do not support error labeling. To overcome these issues, we propose SQL-Checker a specialized model for Text-to-SQL error detection. We first analyze common error factors in Text-to-SQL, and we design a novel data synthesis framework based on these error factors. This framework simulates error factors to construct a basic error SQL data, and then using an error analysis template to distill high-quality SQL error analysis data from large-scale models. For complex errors, a self-guided iterative distillation strategy further enhances data quality. SQL-Checker is then trained on this distilled dataset. Additionally, we refine SQL error labeling and, integrate error label recognition into the detection task, enabling macro-level cause analysis. Experiments show SQL-Checker achieves state-of-the-art results on multiple error detection datasets. Incorporating SQL-Checker into the Text-to-SQL pipeline also improves execution accuracy.

In this paper, we propose an optimized method for road damage detection using a lightweight YOLO model as the baseline. Our approach incorporates a set of effective strategies, including lightweight attention mechanisms, data augmentation, dynamic sampling, weight averaging, and multi-step knowledge distillation. Our method significantly improves inference speed while maintaining high accuracy compared to previous mainstream ensemble-based methods. Our approach achieves notable success in the IEEE Big Data 2024 Optimized Road Damage Detection Challenge (ORDDC’2024), securing second place with an F1 score of 0.7013 and an inference time of 0.0328 s per image. These results demonstrate a strong balance between accuracy and efficiency. Extensive experiments confirm that our method boosts detection accuracy and greatly accelerates inference, making it highly suitable for real-world applications. The source code and trained model are available at https://github.com/YinglongDu/ShiYu_Kunchuan_ORDDC2024.

With the growth of the city and the increase in the number of cars, the maintenance and management of roads attract more attention. Road damage detection of road images is the basic step of road maintenance. To reduce the cost of labor, it is crucial to make the best use of road damage images from different geographical environments and capturing devices. This paper describes our 1-st place solution used in the Crowd sensing-based Road Damage Detection Challenge of the 2022 IEEE International Conference on Big Data. We use YOLO-series models and Faster RCNN as our one-stage and two-stage baseline models respectively. Our model only needs to be trained directly on the datasets of the overall six countries. Besides, with ensemble learning and test time augmentation, our ensemble model achieves the best results on the learderboard of each single country (India, Japan, United States, and Norway) without fine-tuning. Our ensemble model achieves the F1 scores of 0.7699 and 0.7160 on Overall and Average leaderboard, which significantly outperformed the 2-nd p lace F1 s cores of 0.7432 and 0.6744. The source code and trained model are available at https://github.com/berry-ding/ShiYu_SeaView_GRDDC2022.

Scene text recognition has attracted rapidly increasing attention from the research community. Recent dominant approaches typically follow an attention-based encoder-decoder framework that uses a unidirectional decoder to perform decoding in a left-to-right manner, but ignoring equally important right-to-left grammar information. In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition. Firstly, the backward decoder performs decoding from right to left and generates the reverse language context. After that, the forward decoder simultaneously utilizes the visual context from image encoder and the reverse language context from backward decoder through two attention modules. In this way, the bidirectional decoders perform effective interaction to fully fuse the bidirectional grammar information and further improve the decoding quality. Besides, in order to relieve the adverse effect of noises, we devise a gated context mechanism to adaptively make use of the visual context and reverse language context. Extensive experiments on various challenging benchmarks demonstrate the effectiveness of our method.

With the development of Multimedia and Internet techniques, massively increasing visual data, such as image and video, need to be shown and browsed as thumbnails in various digital display platforms, like PC, cell phone, etc. This demonstration presents a grid based adaptive media thumb-nailing approach to maximize user experience in mobile image and video browsing. After representative frame extraction by spectral clustering and salient region detection, we obtain thumbnails with three resizing operators: cropping, warping and scaling, and adaptively fuse them into a unified grid based convex programming problem which could be solved simultaneously and efficiently through numerical optimization. Extensive experiments and comparisons on HUAWEI Honor 6 and Samsung S5 demonstrate that the proposed method achieves an excellent information preservation for thumbnails in mobile devices.

Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users' multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between different behaviors and ignore the heterogeneity of them, which may make improper information transferred and harm the recommendation results. To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors. Thus, the correlation and heterogeneity of multiple behaviors can be modeled by these shared and specific latent factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better.