23 citations · 27 across the 15 of their papers we have counts for
12 papers · 1 filter
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…
Parametric PerceptNet: A bio-inspired deep-net trained for Image Quality Assessment
Jorge Vila-Tomás, Pablo Hernández-Cámara, Valero Laparra +1
Human vision models are at the core of image processing. For instance, classical approaches to the problem of image quality are based on models that include knowledge about human v…
Image Segmentation via Divisive Normalization: dealing with environmental diversity
Pablo Hernández-Cámara, Jorge Vila-Tomás, Paula Dauden-Oliver +3
Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of fai…
Analysis of Deep Image Quality Models
Pablo Hernández-Cámara, Jorge Vila-Tomás, Valero Laparra +1
Subjective image quality measures based on deep neural networks are very related to models of visual neuroscience. This connection benefits engineering but, more interestingly, the…
Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset
Pablo Hernández-Cámara, Valero Laparra, Jesús Malo
One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensor…