The dual thinking framework considers fast, intuitive processing and slower, logical processing. The perception of dual thinking in vision requires images where inferences from intuitive and logical processing differ. We introduce an adversarial dataset to provide evidence for the dual thinking framework in human vision, which also aids in studying the qualitative behavior of deep learning models. Our study also addresses a major criticism of using classification models as computational models of human vision by using instance segmentation models that localize objects. The evidence underscores the importance of shape in identifying instances in human vision and shows that deep learning models lack an understanding of sub-structures, as indicated by errors related to the position and number of sub-components. Additionally, the similarity in errors made by models and intuitive human processing indicates that models only address intuitive thinking in human vision.

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-ofdistribution classification, and decoder method), accessible at https://github.

Human intelligence seems so mysterious that we have not successfully understood its foundation until now. Here, I want to present a basic cognitive process, motion mapping cognition (MMC), which should be a nondecomposable primary function in human vision. Wherein, I point out that, MMC process can be used to explain most of human visual functions in fundamental, but can not be effectively modelled by traditional visual processing ways including image segmentation, object recognition, object tracking etc. Furthermore, I state that MMC may be looked as an extension of Chen's theory of topological perception on human vision, and seems to be unsolvable using existing intelligent algorithm skills. Finally, along with the requirements of MMC problem, an interesting computational model, quantized topological matching principle can be derived by developing the idea of optimal transport theory. Above results may give us huge inspiration to develop more robust and interpretable machine vision models.

Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other. We agree with Bowers et al. (2022) that some of the quoted statements at the beginning of their target article about DNNs as "best models" are exaggerated--perhaps some of them bordering on scientific hype (Intemann, 2020). However, only the authors of such exaggerated statements are to blame, not DNNs: Instead of blaming DNNs, perhaps Bowers et al. should have engaged in a critical discussion of the increasingly widespread practice of rewarding impact and boldness over carefulness and modesty that allows hyperbole to flourish in science. This is unfortunate as the target article does mention a number of valid issues with DNNs in vision science and raises a number of valid concerns. For example, we fully agree that human vision is much more than recognising photographs of objects in scenes; we also fully agree there are still a number of important behavioural differences between DNNs and humans even in terms of core object recognition (DiCarlo et al., 2012), i.e. even when recognising photographs of objects in scenes, such as DNNs' adversarial susceptibility (section 4.1.1)

When a human spots a familiar face or an oncoming vehicle, it takes the brain a mere 100 milliseconds (about one-tenth of a second) to identify it and more importantly, place it in the right context so it can be understood, and the individual can react accordingly. Unsurprisingly, computers may be able to do this faster, but are they as accurate as humans in the real world? Not always, and that's a problem, according to a study led by Western neuroimaging expert Marieke Mur. Computers can be taught to process incoming data, like observing faces and cars, using artificial intelligence known as deep neural networks or deep learning. This type of machine learning process uses interconnected nodes or neurons in a layered structure that resembles the human brain.

When a human spots a familiar face or an oncoming vehicle, it takes the brain a mere 100 milliseconds (about one-tenth of a second) to identify it and more importantly, place it in the right context so it can be understood, and the individual can react accordingly. Unsurprisingly, computers may be able to do this faster, but are they as accurate as humans in the real world? Not always, and that's a problem, according to a study led by Western neuroimaging expert Marieke Mur. Computers can be taught to process incoming data, like observing faces and cars, using artificial intelligence known as deep neural networks or deep learning. This type of machine learning process uses interconnected nodes or neurons in a layered structure that resembles the human brain.

Achieving diversity in human vision is one of the major challenges for AI research. In the vast majority of cases, we are better than machines at understanding the world around us. But machines are catching up--slowly but surely. "Within a single day we humans can go from driving a car to free diving, and continue to reading the newspaper and navigating a dense forest--all without a great deal of effort. For a robot, doing the same things would currently be impossible," says Michael Felsberg, professor at Linköping University and one of Sweden's foremost researchers in computer vision and artificial intelligence (AI). That we humans can do all this, and much more, is largely due to vision.

Computer vision is a branch coming under AI, that permits computers and systems to derive meaningful information from digital images, video, and other visual input – and take actions or make recommendations based on that information. Now that AI permits computers to think, computer vision empowers them to see and perceive. On account of breakthroughs in artificial intelligence and renovation in deep learning and neural networks, the field has been able to take great surges in recent years and has been able to transcend humans in some tasks connected to detecting and labeling objects. One of the defining factors behind the growth of computer vision is the sum total of data we produce today that is then used to train and make computer vision better. Computer vision is utilized in industries lining up from energy and utilities to manufacturing and automotive – and the market for computer vision is blooming.

Short answer: Yes, Long answer: No! Indeed, research on adversarial robustness has led to invaluable insights helping us understand and explore different aspects of the problem. Many attacks and defenses have been proposed over the last couple of years. The problem, however, remains largely unsolved and poorly understood. Here, I argue that the current formulation of the problem serves short term goals, and needs to be revised for us to achieve bigger gains. Specifically, the bound on perturbation has created a somewhat contrived setting and needs to be relaxed. This has misled us to focus on model classes that are not expressive enough to begin with. Instead, inspired by human vision and the fact that we rely more on robust features such as shape, vertices, and foreground objects than non-robust features such as texture, efforts should be steered towards looking for significantly different classes of models. Maybe instead of narrowing down on imperceptible adversarial perturbations, we should attack a more general problem which is finding architectures that are simultaneously robust to perceptible perturbations, geometric transformations (e.g. rotation, scaling), image distortions (lighting, blur), and more (e.g. occlusion, shadow). Only then we may be able to solve the problem of adversarial vulnerability.

Artificial intelligence (AI) can use a person's brainwaves to see around corners and create images of objects the human eye can not directly see. Researchers at the University of Glasgow have shown that the computational imaging technique, known as "ghost imaging", can be combined with human vision to reconstruct the image of objects hidden from view by analyzing how the brain processes barely visible reflections on a wall. Ghost imaging has been used before to reveal objects hidden around corners and normally involves beaming laser light onto a surface, around a corner and back to a camera sensor, then using algorithms to decode the scattered returned light to identify the object. For the new study, researchers swapped out the camera for human eyes. Although the researchers previously used human vision in a passive manner to perform ghost imaging, the new work uses the human visual system in an active role by having a person view the light patterns instead of a camera.