# Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

## TL;DR

This study demonstrates that recurrent connections in neural networks significantly improve occluded object recognition by evolving representations towards unoccluded versions, paralleling mechanisms in the visual cortex.

## Contribution

It systematically compares architectures with bottom-up, lateral, and top-down recurrent connections, showing their effectiveness over feedforward models on a novel occlusion dataset.

## Key findings

- Recurrent models outperform feedforward models in occluded object recognition.
- Recurrent connections help shift representations towards unoccluded objects.
- Recurrent architectures improve recognition accuracy in a 3D occlusion environment.

## Abstract

Recurrent connections in the visual cortex are thought to aid object recognition when part of the stimulus is occluded. Here we investigate if and how recurrent connections in artificial neural networks similarly aid object recognition. We systematically test and compare architectures comprised of bottom-up (B), lateral (L) and top-down (T) connections. Performance is evaluated on a novel stereoscopic occluded object recognition dataset. The task consists of recognizing one target digit occluded by multiple occluder digits in a pseudo-3D environment. We find that recurrent models perform significantly better than their feedforward counterparts, which were matched in parametric complexity. Furthermore, we analyze how the network's representation of the stimuli evolves over time due to recurrent connections. We show that the recurrent connections tend to move the network's representation of an occluded digit towards its un-occluded version. Our results suggest that both the brain and artificial neural networks can exploit recurrent connectivity to aid occluded object recognition.