# Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

arXiv: https://arxiv.org/abs/1907.08831 · HTML: https://mlsanity.com/paper/1907.08831/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics](https://mlsanity.com/paper/2106.02749/) (2021)
- [Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization](https://mlsanity.com/paper/2111.07898/) (2021)
- [Handwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance](https://mlsanity.com/paper/2503.06104/) (2025)
- [Recurrent Feedback Improves Recognition of Partially Occluded Objects](https://mlsanity.com/paper/2104.10615/) (2021)
- [Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models](https://mlsanity.com/paper/2508.07115/) (2025)

## Abstract (arXiv)

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.