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TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning
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TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning
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Tabular-image multimodal learning, which integrates structured tabular data with imaging data, holds great promise for a variety of tasks, especially in medical applications. Yet, two key challenges remain: (1) the lack of a standardized, pretrained representation for tabular data, as is commonly available in vision and language domains; and (2) the difficulty of handling missing values in the tabular modality, which are common in real-world medical datasets. To address these issues, we propose the TabPFN-Integrated Multimodal Engine (TIME), a novel multimodal framework that builds on the recently introduced tabular foundation model, TabPFN. TIME leverages TabPFN as a frozen tabular encoder to generate robust, strong embeddings that are naturally resilient to missing data, and combines them with image features from pretrained vision backbones. We explore a range of fusion strategies and tabular encoders, and evaluate our approach on both natural and medical datasets. Extensive experiments demonstrate that TIME consistently outperforms competitive baselines across both complete and incomplete tabular inputs, underscoring its practical value in real-world multimodal learning scenarios.
Forward citations
Cited by 12 Pith papers
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Beyond IID: How General Are Tabular Foundation Models, Really?
Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 1...
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MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
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Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification
DFPL introduces prototype-based disentanglement and alignment modules to preserve fine-grained consistency across heterogeneous modalities for better performance under missing data conditions.
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TabPFN-3: Technical Report
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than Ta...
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TabPFN-3: Technical Report
TabPFN-3 delivers state-of-the-art tabular prediction performance on benchmarks up to 1M rows, is up to 20x faster than prior versions, and introduces test-time scaling that beats non-TabPFN models by hundreds of Elo points.
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MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
MultiModalPFN extends TabPFN with modality projectors, a multi-head gated MLP, and cross-attention pooler to unify tabular and non-tabular inputs, outperforming prior methods on medical and general multimodal datasets.
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TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast productio...
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Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning
TI-Adapter applies embedding-level and bottleneck adapters to achieve competitive or better performance than full fine-tuning on 20 tabular-image datasets while training far fewer parameters.
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Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach
CoMET achieves strong multimodal classification performance by composing frozen modality encoders, PCA compression, and tabular foundation models without any training, reaching state-of-the-art on diverse benchmarks i...
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Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
TCD-derived MOCAIP and HRV features predict chronological age in healthy adults (MAE ~3.7 y), and diseased cohorts show larger errors that the authors interpret as accelerated cerebrovascular aging.
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Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problem
TabPFN-v2, fine-tuned on one 500-node TSP sample in about two minutes, constructs TSP tours reaching 2-5% of Concorde optimality after 2-opt post-processing, across instance sizes 50 to 1000.
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Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
Machine learning models trained on TCD-derived features from healthy subjects predict brain vascular age and indicate accelerated cerebrovascular aging in subjects with stroke, Alzheimer's, and other conditions.
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