Closing the Gap Between High- and Low-Dimensional Models of High-Level Vision

点击翻译按钮获取中文摘要

Our visual perception is highly complex to capture the complexity of our surrounding visualworld. As a consequence of this, recent models of visual perception like deep neural network models became highly complex as well to capture human perception of arbitrary photographs with some success. However, these models do not yet represent objects as separate entities and they use features designed for other purposes like object recognition or texture generation. Furthermore, their high dimensionality creates statistical problems. These aspects create a gap between these high-dimensional models which work on natural stimuli and the low-dimensional, object-based models we understand. With this project I aim to close this gap by working from both sides. Starting from the side of high-dimensional models, my first aim is to improve our methods to compare high-dimensional models to simpler models. Here I want to introduce modern sparsity based statistical methods, which were specifically designed for high-dimensional problems. Furthermore, I hope to improve upon the representational similarity analysis—one of the few existing methods for high-dimensional models and data. Starting from the side of simpler, interpretable models, I want to work on perceptual organization by generalizing interpretable models to dead-leaves stimuli, which consist of randomly superimposed simple shapes. These stimuli are interesting, because they are analytically tractable image stimuli, which nonetheless isolate the perceptual organization problem. I plan to use these stimuli to perform a behavioural experiment in humans to test our capability for perceptual organization. Modelling these results will result in a broad range of models to apply the methods from aim 1. Finally, my third aim is to use the insights I gain from my first two aims to improve our models of high-level perception of natural stimuli. I am going to use the improved statistical methods from my first aim to prune and recombine existing deep learning models to improve their capabilities as models of human visual perception. Furthermore, I am going to further generalise insights I gain about objects, occlusion and segmentation from the dead-leaves stimuli to natural images. Thus, the results of this project may contribute to better image-computable models of human perception, which can separate the visual world into objects and whose internal workings are explicitly selected to model human processing.