Researchers have discovered a significant gap in neural network visual perception, highlighting the limitations of modern computer vision systems. The study, titled "Degraded Polygons Expose Gaps in Neural Network Visual Perception," explored how well neural networks can classify regular polygons with varying levels of degradation along their edges. Surprisingly, the networks' behavior conflicted with human visual perception and recovery abilities on this seemingly simple task.

The study's findings raise fundamental questions about the robustness and learning capabilities of modern computer vision models. While neural networks excel at many tasks, they struggle to handle degradation and missing information in a way that humans can. This highlights the need for continued progress in developing AI systems that can match and surpass human-level visual understanding.

Source: https://dev.to/mikeyoung44/degraded-polygons-expose-gaps-in-neural-network-visual-perception-2bb