7 citations · 17 across the 24 of their papers we have counts for
30 papers
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…
The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models
Fabio Arnez, Alexandra Gomez-Villa
Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performance rather than a normative pr…
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…
Online Continual Learning with Dynamic Label Hierarchies
Xinrui Wang, Shao-Yuan Li, Bartłomiej Twardowski +2
Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical org…
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…
CLA: Latent Alignment for Online Continual Self-Supervised Learning
Giacomo Cignoni, Andrea Cossu, Alexandra Gomez-Villa +2
Self-supervised learning (SSL) is able to build latent representations that generalize well to unseen data. However, only a few SSL techniques exist for the online CL setting, wher…
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…
Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models
Héctor Laria, Alexandra Gomez-Villa, Jiang Qin +5
Recent advances in text-to-image (T2I) diffusion models have enabled remarkable control over various attributes, yet precise color specification remains a fundamental challenge. Ex…
Replay-free Online Continual Learning with Self-Supervised MultiPatches
Giacomo Cignoni, Andrea Cossu, Alex Gomez-Villa +2
Online Continual Learning (OCL) methods train a model on a non-stationary data stream where only a few examples are available at a time, often leveraging replay strategies. However…
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…
At the edge of a generative cultural precipice
Diego Porres, Alex Gomez-Villa
Since NFTs and large generative models (such as DALLE2 and Stable Diffusion) have been publicly available, artists have seen their jobs threatened and stolen. While artists depend…
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang +1
Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL met…