# DASHA: Decentralized Autofocusing System with Hierarchical Agents

arXiv: https://arxiv.org/abs/2108.12842 · HTML: https://mlsanity.com/paper/2108.12842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Learning Single Camera Depth Estimation using Dual-Pixels](https://mlsanity.com/paper/1904.05822/) (2019)
- [Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?](https://mlsanity.com/paper/2003.01629/) (2020)
- [Learning to Autofocus](https://mlsanity.com/paper/2004.12260/) (2020)
- [Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?](https://mlsanity.com/paper/1809.01567/) (2018)
- [Bridging the Gap Between Computational Photography and Visual Recognition](https://mlsanity.com/paper/1901.09482/) (2019)
- [Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning](https://mlsanity.com/paper/1803.02269/) (2018)
- [A Robotic Auto-Focus System based on Deep Reinforcement Learning](https://mlsanity.com/paper/1809.03314/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing](https://mlsanity.com/paper/2307.11638/) (2023)

## Abstract (arXiv)

State-of-the-art object detection models are frequently trained offline using available datasets, such as ImageNet: large and overly diverse data that are unbalanced and hard to cluster semantically. This kind of training drops the object detection performance should the change in illumination, in the environmental conditions (e.g., rain), or in the lens positioning (out-of-focus blur) occur. We propose a decentralized hierarchical multi-agent deep reinforcement learning approach for intelligently controlling the camera and the lens focusing settings, leading to a significant improvement beyond the capacity of the popular detection models (YOLO, Faster R-CNN, and Retina are considered). The algorithm relies on the latent representation of the camera's stream and, thus, it is the first method to allow a completely no-reference tuning of the camera, where the system trains itself to auto-focus itself.