Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

Highlights•Naturalistic scene processing is highly efficient.•Scene context modulates
perceptual and semantic object processing.•Priors about natural environments guide …

The semantic image segmentation task presents a trade-off between test time accuracy and
training time annotation cost. Detailed per-pixel annotations enable training accurate …

The long-standing goal of localizing every object in an image remains elusive. Manually
annotating objects is quite expensive despite crowd engineering innovations. Current state …

We live in a rich, three dimensional world with complex arrangements of meaningful objects.
For decades, however, theories of visual attention and perception have been based on …

Visual scene analysis in humans has been characterized by the presence of regions in
extrastriate cortex that are selectively responsive to scenes compared with objects or faces …

A central regularity of visual perception is the co-occurrence of objects in the natural
environment. Here we use machine learning and fMRI to test the hypothesis that object co …

Why are human inferences sometimes remarkably close to the Bayesian ideal and other
times systematically biased? In particular, why do humans make near-rational inferences in …

Successful adaptive behavior requires efficient attentional and locomotive systems. Previous
research has thoroughly investigated how we achieve this efficiency during natural behavior …

Understanding the content of memory is essential to teasing apart its underlying
mechanisms. While recognition tests have commonly been used to probe memory, it is …