Publications by authors named "Alex Gomez-Villa"
Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans.
View Article and Find Full Text PDFThree decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the contrast sensitivity functions (CSFs) in light of 1) the current trend of using artificial neural networks for studying vision, and 2) the current knowledge of retinal image representations.
View Article and Find Full Text PDFArticle Synopsis
- Visual neurons and visual perception are complex and often nonlinear, contradicting most current vision models that use a simpler linear receptive field (RF).
- The traditional linear RF has limitations, like changing with different inputs and not aligning with recent findings about how neurons process information.
- The proposed intrinsically nonlinear receptive field (INRF) offers a more accurate model, maintaining consistency across various stimuli, leading to better performance in artificial neural networks and suggesting a shift in both vision science and artificial intelligence approaches.