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.
Related stories
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 Machine Learning
5d ago
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
6d ago
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 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.
arXiv AI
Aug 11
UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
arXiv Machine Learning
Jul 2
Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.
arXiv AI
Jun 26
Semantic Generative Tuning for Unified Multimodal Models
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
arXiv Machine Learning
Jul 21
MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.
arXiv AI
Jul 29
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
arXiv:2607. 25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two capabilities.
arXiv Machine Learning
Aug 3
GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System
arXiv:2607. 29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of multimodal signals and adaptation to evolving user intent.