7 citations · 17 across the 24 of their papers we have counts for
22 papers · 1 filter
On Color Alignment in VAE Latent Spaces and Its Applications
Julian D. Santamaria, Kai Wang, Jesús Malo +2
Variational autoencoders (VAEs) are a key part of modern text-to-image models, which generate images within their latent space. VAEs are known to disentangle the main factors of va…
DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization
Tao Wu, Alexandra Gomez-Villa, Senmao Li +3
Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line of work has exploited these s…
BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation
Junyu Li, Qiuyu Chen, Pengcheng Wang +5
Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challenging, especially when the input…
Do Vision Encoders Exhibit Human-like Color Thresholds?
Engy Ehab, Pablo Hernández-Cámara, Nahla Belal +3
Understanding and characterizing human color perception is a longstanding research goal. One of the most traditional approaches is looking for the human color discrimination thresh…
DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation
Héctor Laria, Yiping Han, Julian D. Santamaria +4
Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue thi…
NumColor: Precise Numeric Color Control in Text-to-Image Generation
Muhammad Atif Butt, Diego Hernandez, Alexandra Gomez-Villa +3
Text-to-image diffusion models excel at generating images from natural language descriptions, yet fail to interpret numerical colors such as hex codes (#FF5733) and RGB values (rgb…
Evaluating and Preserving High-level Fidelity in Super-Resolution
Josep M. Rocafort, Shaolin Su, Alexandra Gomez-Villa +1
Recent image Super-Resolution (SR) models are achieving impressive effects in reconstructing details and delivering visually pleasant outputs. However, the overpowering generative…
GenColorBench: A Color Evaluation Benchmark for Text-to-Image Generation Models
Muhammad Atif Butt, Alexandra Gomez-Villa, Tao Wu +3
Recent years have seen impressive advances in text-to-image generation, with image generative or unified models producing high-quality images from text. Yet these models still stru…
Color Names in Vision-Language Models
Alexandra Gomez-Villa, Pablo Hernández-Cámara, Muhammad Atif Butt +3
Color serves as a fundamental dimension of human visual perception and a primary means of communicating about objects and scenes. As vision-language models (VLMs) become increasing…
Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy
Aymen Bouguerra, Daniel Montoya, Alexandra Gomez-Villa +2
Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OOD) detection. However, their hig…
Contrast Sensitivity in Multimodal Large Language Models: A Psychophysics-Inspired Evaluation
Pablo Hernández-Cámara, Alexandra Gomez-Villa, Jose Manuel Jaén-Lorites +3
Understanding how Multimodal Large Language Models (MLLMs) process low-level visual features is critical for evaluating their perceptual abilities and has not been systematically c…
On the dynamic evolution of CLIP texture-shape bias and its relationship to human alignment and model robustness
Pablo Hernández-Cámara, Jose Manuel Jaén-Lorites, Alexandra Gómez-Villa +3
Contrastive language-image models such as CLIP have demonstrated remarkable generalization capabilities. However, how their internal visual representations evolve during training a…
Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
Yuyang Liu, Qiuhe Hong, Linlan Huang +6
Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerfu…
FindMeIfYouCan: Bringing Open Set metrics to , and Out-of-Distribution Object Detection
Daniel Montoya, Aymen Bouguerra, Alexandra Gomez-Villa +1
State-of-the-art Object Detection (OD) methods predominantly operate under a closed-world assumption, where test-time categories match those encountered during training. However, d…
Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
Jiang Qin, Senmao Li, Alexandra Gomez-Villa +4
Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation using only a few style reference ima…
Assessing Open-world Forgetting in Generative Image Model Customization
Héctor Laria, Alex Gomez-Villa, Kai Wang +2
Recent advances in diffusion models have significantly enhanced image generation capabilities. However, customizing these models with new classes often leads to unintended conseque…
Exemplar-free Continual Representation Learning via Learnable Drift Compensation
Alex Gomez-Villa, Dipam Goswami, Kai Wang +3
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation le…
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training
Simone Zini, Alex Gomez-Villa, Marco Buzzelli +3
Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used ar…
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu +2
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable t…
Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications
A. Gomez-Villa, A. Martín, J. Vazquez-Corral +2
Visual illusions allow researchers to devise and test new models of visual perception. Here we show that artificial neural networks trained for basic visual tasks in natural images…
Synthesizing Visual Illusions Using Generative Adversarial Networks
Alexander Gomez-Villa, Adrian Martín, Javier Vazquez-Corral +2
Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system. In this work we intro…
Convolutional Neural Networks Deceived by Visual Illusions
Alexander Gomez-Villa, Adrián Martín, Javier Vazquez-Corral +1
Visual illusions teach us that what we see is not always what it is represented in the physical world. Its special nature make them a fascinating tool to test and validate any new…