arXiv subjects

Jose Manuel Jaén-Lorites

Publications and source records attributed to Jose Manuel Jaén-Lorites.

4 recordsLinked to original sources

arXiv2025

On the dynamic evolution of CLIP texture-shape bias and its relationship to human alignment and model robustness

Contrastive language-image models such as CLIP have demonstrated remarkable generalization capabilities. However, how their internal visual representations evolve during training and how this evolution relates to human perception remains poorly understood. Most existing analysis characterize fully trained models, leaving the dynamics of representational biases and perceptual alignment largely unexplored. In this work, we present an epoch-by-epoch analysis of CLIP models throughout training, focusing on the evolution of texture-shape bias, alignment with human perceptual judgements, and sensitivity to image noise. Using multiple perceptual benchmarks spanning low-level image quality assessment, mid-level perceptual similarity, saliency correspondence, and noisy robustness, we identify a consistent, training-stage-dependent representational transition. Early training stages exhibit strong texture bias, elevated alignment with low-level human perceptual measures, and increased sensitivity to Gaussian noise perturbations. As training progresses, this texture bias gradually diminishes in favor of more shape-based representations, coinciding with improved robustness to noise and a decline in low-level perceptual alignment. Importantly, these dynamics are consistently observed across multiple CLIP model scales, indicating that the phenomenon is not specific to a particular architecture size. Our findings provide an empirical characterization of how perceptual alignment, feature bias, and robustness co-evolve during multimodal model training. This work reveals a systematic trade-off between early low-level perceptual alignment and later robustness, offering new insights into the representational dynamics of vision-language models and their relationship to human visual processing.

cs.CV↗

arXiv2025

Contrast Sensitivity in Multimodal Large Language Models: A Psychophysics-Inspired Evaluation

Understanding how Multimodal Large Language Models (MLLMs) process low-level visual features is critical for evaluating their perceptual abilities and has not been systematically characterized. Inspired by human psychophysics, we introduce a behavioural method for estimating the Contrast Sensitivity Function (CSF) in MLLMs by treating them as end-to-end observers. Models are queried with structured prompts while viewing noise-based stimuli filtered at specific spatial frequencies. Psychometric functions are derived from the binary verbal responses, and contrast thresholds (and CSFs) are obtained without relying on internal activations or classifier-based proxies. Our results reveal that some models resemble human CSFs in shape or scale, but none capture both. We also find that CSF estimates are highly sensitive to prompt phrasing, indicating limited linguistic robustness. Finally, we show that CSFs predict model performance under frequency-filtered and adversarial conditions. These findings highlight systematic differences in frequency tuning across MLLMs and establish CSF estimation as a scalable diagnostic tool for multimodal perception.

cs.CV↗

arXiv2025

Hues and Cues: Human vs. CLIP

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-like nature of artificial models. The objective of this work is proposing a new approach to evaluate artificial models via board games. To this effect, we test the color perception and color naming capabilities of CLIP by playing the board game Hues & Cues and assess its alignment with humans. Our experiments show that CLIP is generally well aligned with human observers, but our approach brings to light certain cultural biases and inconsistencies when dealing with different abstraction levels that are hard to identify with other testing strategies. Our findings indicate that assessing models with different tasks like board games can make certain deficiencies in the models stand out in ways that are difficult to test with the commonly used benchmarks.

cs.CV↗

arXiv2025

Do Vision Transformers See Like Humans? Evaluating their Perceptual Alignment

Vision Transformers (ViTs) achieve remarkable performance in image recognition tasks, yet their alignment with human perception remains largely unexplored. This study systematically analyzes how model size, dataset size, data augmentation and regularization impact ViT perceptual alignment with human judgments on the TID2013 dataset. Our findings confirm that larger models exhibit lower perceptual alignment, consistent with previous works. Increasing dataset diversity has a minimal impact, but exposing models to the same images more times reduces alignment. Stronger data augmentation and regularization further decrease alignment, especially in models exposed to repeated training cycles. These results highlight a trade-off between model complexity, training strategies, and alignment with human perception, raising important considerations for applications requiring human-like visual understanding.

cs.CV↗