arXiv Machine Learning
Jul 10
The Importance of Encoder Choice:A Tabular-Image Study
arXiv:2607. 07756v1 Announce Type: new Abstract: Multimodal learning usually requires a dedicated encoder per modality.
Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning
arXiv:2606. 11682v1 Announce Type: cross Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data.
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arXiv Machine Learning
Aug 5
Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts
arXiv:2608. 03557v1 Announce Type: cross Abstract: Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models.
arXiv Machine Learning
Jul 14
TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings
arXiv:2607. 11007v1 Announce Type: new Abstract: Few-shot multimodal classification commonly attaches a lightweight head, such as $k$-nearest neighbors, logistic regression, or a linear SVM, to a frozen pretrained encoder.
arXiv AI
3d ago
ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining
arXiv:2603.02767v4 Announce Type: replace-cross Abstract: Image--text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield repre...
arXiv AI
4d ago
AdaKerNet: Neural Kernel Decoding for Task-Adaptive Prediction with Multimodal Large Models
AdaKerNet is a task‑adaptive neural kernel decoder that operates on frozen multimodal representations from large foundation models, without requiring access to the models’ parameters. It learns Lipschitz‑controlled multimodal features, a reference kernel providing a soft structural prior, and a lightweight nonlinear predictor that deforms this structure. Experiments on four multimodal large language models and diverse input modalities show consistent improvements over baseline decoders, achieving up to 41% error reduction in scarce‑label settings.
arXiv Machine Learning
Aug 21
Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics
arXiv:2412. 06265v3 Announce Type: replace Abstract: Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointly.
arXiv Machine Learning
Sep 23
MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction
MMAP is a Multimodal Missing‑Aware Alignment Pretraining method designed to learn image‑tabular representations from incomplete data. It uses a sigmoid contrastive learning image encoder with generative reconstruction, a tabular encoder based on a foundation model, and a missing token generator to handle missing modalities. The approach is evaluated on longitudinal Alzheimer’s tasks—predicting disease stage conversion and amyloid status—and outperforms both multimodal and unimodal baselines.
arXiv Computer Vision
Sep 22
Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
arXiv:2609.22271v1 Announce Type: new Abstract: Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes...
arXiv Machine Learning
Aug 14
TabSOM: A tabular-to-image encoding method based on self-organizing maps
arXiv:2608. 13513v1 Announce Type: cross Abstract: Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers.
Hugging Face Trending Papers
Aug 13
TabSOM: A tabular-to-image encoding method based on self-organizing maps
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.
arXiv Computer Vision
Sep 16
FLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation
FLAT (Flexible‑Length Aligned Transmodal representations) is a joint multimodal pre‑training framework that learns a shared encoder for images and text, producing 1‑D continuous embeddings that can be directly used by downstream generative decoders. By combining contrastive alignment with bidirectional cross‑modal generative objectives, FLAT yields representations that are both discriminative and generative, enabling cross‑modal retrieval and generation with a single pre‑training stage. The model achieves strong performance on T2I generation (GenEval 71.1), image captioning (BLEU‑4 40.5, CIDEr 138.6), and retrieval tasks (Recall@5 86.8/75.8 on MS‑COCO, 98.3/93.6 on Flickr30K), and supports linear interpolation, latent space arithmetic, and zero‑shot composed retrieval.
arXiv AI
Jul 7
Transferability Between Understanding and Generation in Unified Multimodal Models
arXiv:2607. 04423v1 Announce Type: cross Abstract: Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied.