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Ran Jia

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3 papers

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IJCAI Conference 2025 Conference Paper

IE-PMMA: Point Cloud Completion Through Inverse Edge-aware Upsampling and Precise Multi-Modal Feature Alignment

Ran Jia
Junpeng Xue
Shuai Ma
Wenbo Lu
Kelei Wang

Point cloud completion is a crucial task in 3D computer vision. Multi-modal completion approaches have gained attention among the popular two-stage point cloud completion methods. However, there is a notable lack of research focused on accurately aligning data from different modalities within these methods. Additionally, in other point cloud-based tasks, edge point information often provides unexpected positive contributions. In this paper, we propose a novel point cloud completion method that leverages edge point information for the first time in the completion task, which also addresses the precise alignment of multi-modal data. In particular, we implement a two-step local-to-global module to achieve better alignment of multi-modal data during the preliminary point cloud generation process. Besides, we introduce a new spatial representation structure capable of extracting a fixed number of edge points. Moreover, with the assistance of edge information, we further design an inverse edge-aware upsampler to refine the point cloud. We evaluate our method on three typical datasets, and the results demonstrate that our IE-PMMA outperforms the existing state-of-the-art methods quantitatively and visually.

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AAAI Conference 2024 Conference Paper

Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries

Xinyi He
Mengyu Zhou
Xinrun Xu
Xiaojun Ma
Rui Ding
Lun Du
Yan Gao
Ran Jia

Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.

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AAAI Conference 2023 Conference Paper

SheetPT: Spreadsheet Pre-training Based on Hierarchical Attention Network

Ran Jia
Qiyu Li
Zihan Xu
Xiaoyuan Jin
Lun Du
Haoyu Dong
Xiao Lv
Shi Han

Spreadsheets are an important and unique type of business document for data storage, analysis and presentation. The distinction between spreadsheets and most other types of digital documents lies in that spreadsheets provide users with high flexibility of data organization on the grid. Existing related techniques mainly focus on the tabular data and are incompetent in understanding the entire sheet. On the one hand, spreadsheets have no explicit separation across tabular data and other information, leaving a gap for the deployment of such techniques. On the other hand, pervasive data dependence and semantic relations across the sheet require comprehensive modeling of all the information rather than only the tables. In this paper, we propose SheetPT, the first pre-training technique on spreadsheets to enable effective representation learning under this scenario. For computational effectiveness and efficiency, we propose the coherent chunk, an intermediate semantic unit of sheet structure; and we accordingly devise a hierarchical attention-based architecture to capture contextual information across different structural granularities. Three pre-training objectives are also designed to ensure sufficient training against millions of spreadsheets. Two representative downstream tasks, formula prediction and sheet structure recognition are utilized to evaluate its capability and the prominent results reveal its superiority over existing state-of-the-art methods.

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