7 citations · 7 across the 10 of their papers we have counts for
12 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…
Human-AI Perceptual Alignment by Playing Hues and Cues
Nuria Alabau-Bosque, Jorge Vila-Tomás, Paula Daudén-Oliver +3
Evaluating the perceptual alignment between Contrastive Vision-Language Models (CVLMs) and humans is typically constrained by traditional benchmarks that overlook fine-grained sema…
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…
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…
Hues and Cues: Human vs. CLIP
Nuria Alabau-Bosque, Jorge Vila-Tomás, Paula Daudén-Oliver +4
Playing games is inherently human, and a lot of games are created to challenge different human characteristics. However, these tasks are often left out when evaluating the human-li…
From Images to Perception: Emergence of Perceptual Properties by Reconstructing Images
Pablo Hernández-Cámara, Jesus Malo, Valero Laparra
A number of scientists suggested that human visual perception may emerge from image statistics, shaping efficient neural representations in early vision. In this work, a bio-inspir…
Do Vision Transformers See Like Humans? Evaluating their Perceptual Alignment
Pablo Hernández-Cámara, Jose Manuel Jaén-Lorites, Jorge Vila-Tomás +2
Vision Transformers (ViTs) achieve remarkable performance in image recognition tasks, yet their alignment with human perception remains largely unexplored. This study systematicall…
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…
A Turing Test for Artificial Nets devoted to model Human Vision
Jorge Vila-Tomás, Pablo Hernández-Cámara, Qiang Li +2
In our invited talk at the AI Evaluation Workshop of the University of Bristol back in June 2022 we argued that, despite claims about successful modeling of the visual brain using…
The Art of Deception: Color Visual Illusions and Diffusion Models
Alex Gomez-Villa, Kai Wang, Alejandro C. Parraga +4
Visual illusions in humans arise when interpreting out-of-distribution stimuli: if the observer is adapted to certain statistics, perception of outliers deviates from reality. Rece…
Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks
Qiang Li, Vince D. Calhoun, Adithya Ram Ballem +3
The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for…