8 papers
TabH2O: A Unified Foundation Model for Tabular Prediction
Pascal Pfeiffer, Dmitry Gordeev, Mathias Müller +8
We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning. TabH2O builds on the TabICL arc…
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
Omar Elezabi, Marcos V. Conde, Zongwei Wu +1
Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly st…
Streaming Neural Images
Marcos V. Conde, Andy Bigos, Radu Timofte
Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. INRs offer unprecedented adva…
Extreme Compression of Adaptive Neural Images
Leo Hoshikawa, Marcos V. Conde, Takeshi Ohashi +1
Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm for signal representation, from images and audio to 3D scenes and videos. The fundamental idea is to r…
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Marcos V. Conde, Radu Timofte, Zihao Lu +36
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-R…
RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
Marcos V. Conde, Radu Timofte, Radu Berdan +30
Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensiv…