Evaluating deep neural networks (DNNs) as models of human perception has given rich insights into both human visual processing and representational properties of DNNs. We extend this work by analyzing how well DNNs perform compared to humans when constrained by peripheral vision -- which limits human performance on a variety of tasks, but also benefits the visual system significantly. We evaluate this by (1) modifying the Texture Tiling Model (TTM), a well tested model of peripheral vision to be more flexibly used with DNNs, (2) generating a large dataset which we call COCO-Periph that contains images transformed to capture the information available in human peripheral vision, and (3) comparing DNNs to humans at peripheral object detection using a psychophysics experiment. Our results show that common DNNs underperform at object detection compared to humans when simulating peripheral vision with TTM. Training on COCO-Periph begins to reduce the gap between human and DNN performance and leads to small increases in corruption robustness, but DNNs still struggle to capture human-like sensitivity to peripheral clutter. Our work brings us closer to accurately modeling human vision, and paves the way for DNNs to mimic and sometimes benefit from properties of human visual processing.

Computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer’s visual field, of particular importance with the rise of high-resolution and wide field-of-view display devices. However, while variations in acuity and contrast sensitivity across the field of view have been well-studied and modeled, a more consequential variation concerns peripheral vision’s degradation in the face of clutter, known as crowding. Understanding of peripheral crowding has greatly advanced in recent years, in terms of both phenomenology and modeling. Accurately leveraging this knowledge is critical for many applications, as peripheral vision covers a majority of pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, researchers have recently developed high-performing models of peripheral crowding, known as “pooling” models, which predict a wide range of phenomena but are computationally inefficient. We reformulate the problem as a dataflow computation, which enables faster processing and operating on larger images. Further, we account for the explicit encoding of “end stopped” features in the image, which was missing from previous methods. We evaluate our model in the context of perception of textures in the periphery, including a novel texture dataset and updated textural descriptors. Our improved computational framework may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics.

This paper describes an approach to solving insertion tasks by a robot that uses exploratory behaviors and proprioceptive feedback. The approach was inspired by the developmental progression of insertion abilities in both chimpanzees and humans (Hayashi et al. 2006). Before mastering insertions, the infants of the two species undergo a stage where they only press objects against other objects without releasing them. Our goal was to emulate this developmental stage on a robot to see if it may lead to simpler representations for insertion tasks. Experiments were performed using a shapesorter puzzle with three different blocks and holes.