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Jun-Peng Jiang

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6 papers

2 author rows

AAAI Conference 2026 Short Paper

Multimodal Tabular Data Learning

Jun-Peng Jiang

Tabular data is a fundamental form of information in real-world applications, ranging from finance and healthcare to scientific research. Unlike traditional views that treat tables as isolated structured data, tables are often inherently multimodal—appearing as images, embedded in documents, or coexisting with text and other modalities. My research explores multimodal tabular data learning, aiming to bridge structured tabular knowledge with diverse input forms and tasks. To this end, our work investigates leveraging tabular data as expert knowledge to provide guidance for visual modalities and enable cross-modal transfer learning. We also study more common scenarios where tables appear as images, conducting comprehensive investigations from evaluation to method development for table-based question answering and reasoning. Beyond these works, we extend tabular learning to more general scenarios, developing unified models capable of handling diverse table tasks within a single framework, and further expanding from tables to broader document-level parsing and understanding.

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ICML Conference 2025 Conference Paper

Compositional Condition Question Answering in Tabular Understanding

Jun-Peng Jiang
Tao Zhou
De-Chuan Zhan
Han-Jia Ye

Multimodal Large Language Models (MLLMs) for tabular understanding have made significant progress in tasks such as financial report analysis and public data tests. However, our comprehensive analysis shows that these models are still limited in certain simple scenarios, particularly when handling compositional conditions in QA. Further investigation reveals that the poor performance can be attributed to two main challenges: the visual encoder’s inability to accurately recognize the content of a row, and the model’s tendency to overlook conditions in the question. To address these, we introduce a new Compositional Condition Tabular Understanding method, called CoCoTab. Specifically, to capture the structural relationships within tables, we enhance the visual encoder with additional row and column patches. Moreover, we introduce the conditional tokens between the visual patches and query embeddings, ensuring the model focuses on relevant parts of the table according to the conditions specified in the query. Additionally, we also introduce the Massive Multimodal Tabular Understanding (MMTU) benchmark, which comprehensively assesses the full capabilities of MLLMs in tabular understanding. Our proposed method achieves state-of-the-art performance on both existing tabular understanding benchmarks and MMTU. Our code can be available at https: //github. com/LAMDA-Tabular/MMTU.

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NeurIPS Conference 2025 Conference Paper

Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation

Zhi-Kai Chen
Jun-Peng Jiang
Han-Jia Ye
De-Chuan Zhan

Autoregressive (AR) image generation models are capable of producing high-fidelity images but often suffer from slow inference due to their inherently sequential, token-by-token decoding process. Speculative decoding, which employs a lightweight draft model to approximate the output of a larger AR model, has shown promise in accelerating text generation without compromising quality. However, its application to image generation remains largely underexplored. The challenges stem from a significantly larger sampling space, which complicates the alignment between the draft and target model outputs, coupled with the inadequate use of the two-dimensional spatial structure inherent in images, thereby limiting the modeling of local dependencies. To overcome these challenges, we introduce Hawk, a new approach that harnesses the spatial structure of images to guide the speculative model toward more accurate and efficient predictions. Experimental results on multiple text-to-image benchmarks demonstrate a 1. 71× speedup over standard AR models, while preserving both image fidelity and diversity.

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NeurIPS Conference 2025 Conference Paper

Multimodal Tabular Reasoning with Privileged Structured Information

Jun-Peng Jiang
Yu Xia
Hai-Long Sun
Shiyin Lu
Qingguo Chen
Weihua Luo
Kaifu Zhang
De-Chuan Zhan

Tabular reasoning requires complex, multi-step information extraction and logical inference, such as aggregation, comparison, or calculation over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured text tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning directly from table images. Our core strategy is to leverage privileged structured information---specifically, the ground-truth structured table data available during training but inaccessible at test time---to enhance multimodal large language models (MLLMs). The key challenges lie in: accurately aligning visual representations with the structured information, particularly mapping the visual evidence to logical steps; and effectively transferring the reasoning skills learned during training to the MLLM for visual inference. To address these, we introduce {\sc Turbo} (TabUlar Reasoning with Bridged infOrmation), a new framework for multimodal tabular reasoning using privileged information. {\sc Turbo} benefits from a structure-aware reasoning trace generator based on DeepSeek-R1, which contributes to high-quality modality-bridged information. On this basis, {\sc Turbo} repeatedly generates and selects advantageous reasoning traces, further enhancing the model's tabular reasoning ability. Experimental results demonstrate that, with limited (9k) data, {\sc Turbo} achieves state-of-the-art performance ($+7. 2\%$ vs. previous SOTA) across multiple datasets.

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JMLR Journal 2025 Journal Article

Talent: A Tabular Analytics and Learning Toolbox

Si-Yang Liu
Hao-Run Cai
Qi-Le Zhou
Huai-Hong Yin
Tao Zhou
Jun-Peng Jiang
Han-Jia Ye

Tabular data is a prevalent source in machine learning. While classical methods have proven effective, deep learning methods for tabular data are emerging as flexible alternatives due to their capacity to uncover hidden patterns and capture complex interactions. Considering that deep tabular methods exhibit diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce Talent (Tabular Analytics and Learning Toolbox), a versatile toolbox for utilizing, analyzing, and comparing these methods. Talent includes over 35 deep tabular prediction methods, offering various encoding and normalization modules, all within a unified, easily extensible interface. We demonstrate its design, application, and performance evaluation in case studies. The code is available at https://github.com/LAMDA-Tabular/TALENT. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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ICML Conference 2024 Conference Paper

Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images

Jun-Peng Jiang
Han-Jia Ye
Leye Wang
Yang Yang 0074
Yuan Jiang 0001
De-Chuan Zhan

Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the inherent complexity of accurately mapping diverse tabular data to visual contexts, coupled with the necessity to devise distinct strategies for numerical and categorical tabular attributes. We propose CHannel tAbulaR alignment with optiMal tranSport (Charms), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. Specifically, Charms measures similarity distributions across modalities to effectively differentiate and transfer relevant tabular features, with a focus on morphological characteristics, enhancing the capabilities of visual classifiers. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that Charms not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge.

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