# Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

arXiv: https://arxiv.org/abs/2607.29473 · HTML: https://mlsanity.com/paper/2607.29473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform](https://mlsanity.com/paper/2005.10145/) (2020)

## Abstract (arXiv)

The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem. On one hand, affordance segmentation requires high-level abstraction capabilities, that typically involve large-size models. On the other hand, computing resources hosted on wearable robots prevent to run large-size models in real-time. The paper presents an analysis of the role of the segmentation head in the trade-off between generalization performance and compute cost. The obtained models outperform modern baseline solutions in well-known, real-world datasets while meeting low computing requirements.