activity
20192025
most citedDeep intrinsic decomposition trained on surreal scenes yet with realistic light effects

8 citations · 14 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CV2025★ 2 cited

Color in Visual-Language Models: CLIP deficiencies

Guillem Arias, Ramon Baldrich, Maria Vanrell

This work explores how color is encoded in CLIP (Contrastive Language-Image Pre-training) which is currently the most influential VML (Visual Language model) in Artificial Intellig…

cs.CV2024

MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields

Yixiong Yang, Shilin Hu, Haoyu Wu +3

Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic sce…

cs.CV2024

Relighting from a Single Image: Datasets and Deep Intrinsic-based Architecture

Yixiong Yang, Hassan Ahmed Sial, Ramon Baldrich +1

Single image scene relighting aims to generate a realistic new version of an input image so that it appears to be illuminated by a new target light condition. Although existing wor…

cs.CV2024

Learning Relighting and Intrinsic Decomposition in Neural Radiance Fields

Yixiong Yang, Shilin Hu, Haoyu Wu +3

The task of extracting intrinsic components, such as reflectance and shading, from neural radiance fields is of growing interest. However, current methods largely focus on syntheti…

cs.CV2020★ 4 cited

Intrinsic Decomposition of Document Images In-the-Wild

Sagnik Das, Hassan Ahmed Sial, Ke Ma +3

Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on r…

cs.CV2020

Light Direction and Color Estimation from Single Image with Deep Regression

Hassan A. Sial, Ramon Baldrich, Maria Vanrell +1

We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset wit…