A feedforward mechanism for human-like contour integration
Fig 2
Contour integration capacity in feedforward CNNs.
(A) Accuracy on contour detection for the held-out test set across different readout layers – gray indicating randomly initialized models, orange for models pretrained on Imagenet for object recognition, and blue for finetuned models. Error bars denote 95% confidence intervals for readout accuracy. (B) Saliency maps from two fine-tuned models, highlighting pixel relevance for detecting the contour within an example image; location of the contour is highlighted in red for illustrative purposes. (C) Example image pair with misaligned and aligned contour elements. Contour patches are highlighted for illustrative clarity. (D) Plot showing the fine-tuned model’s sensitivity to elements making up the contour (for aligned display), or elements at the same locations in misaligned display. Each pair is connected via a gray line. Overall the plot depicts the sensitivity to local alignment of contour elements.