The primary goal of the Human-Inspired Computer Vision (HCV) workshop is to bridge the gap between machine perception and biological systems by integrating findings from neuroscience, psychology, and cognitive science. Although modern computer vision achieves impressive results in many tasks, it still lacks the robustness and contextual flexibility inherent to human vision, and the relationship between artificial and human vision remains unclear. Investigating such a relationship is timely and important for two reasons:

Improving machine vision: Insights from psychology, cognitive science, and neuroscience can inform current research towards developing computer vision models that operate in a human-like fashion. This cross-disciplinary approach can help us systematically identify and tackle the performance and generalization gaps between humans and machines in key research areas.

Understanding and enhancing human vision: Modeling biological vision is a hot topic in computational cognitive neuroscience. By developing interpretable computer vision models, we create powerful tools to explain neuroscientific and behavioral observations and to enhance human vision and cognition, e.g. in the presence of sensory or neurodevelopmental disorders.

See the Call for Papers