# Surgical fine-tuning for Grape Bunch Segmentation under Visual Domain Shifts

arXiv: https://arxiv.org/abs/2307.00837 · HTML: https://mlsanity.com/paper/2307.00837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)
- [Surgical Fine-Tuning Improves Adaptation to Distribution Shifts](https://mlsanity.com/paper/2210.11466/) (2022)
- [Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning](https://mlsanity.com/paper/2201.03529/) (2022)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [Combining Local and Global Viewpoint Planning for Fruit Coverage](https://mlsanity.com/paper/2108.08114/) (2021)
- [The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review](https://mlsanity.com/paper/2103.12488/) (2021)

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

- [More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation](https://mlsanity.com/paper/2402.06581/) (2024)

## Abstract (arXiv)

Mobile robots will play a crucial role in the transition towards sustainable agriculture. To autonomously and effectively monitor the state of plants, robots ought to be equipped with visual perception capabilities that are robust to the rapid changes that characterise agricultural settings. In this paper, we focus on the challenging task of segmenting grape bunches from images collected by mobile robots in vineyards. In this context, we present the first study that applies surgical fine-tuning to instance segmentation tasks. We show how selectively tuning only specific model layers can support the adaptation of pre-trained Deep Learning models to newly-collected grape images that introduce visual domain shifts, while also substantially reducing the number of tuned parameters.