From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots

• URL: http://arxiv.org/abs/2410.23906v1
• Date: Thu, 31 Oct 2024 13:11:09 GMT
• Title: From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots
• Authors: Vasileios Tzouras, Lazaros Nalpantidis, Ronja Güldenring,
• Abstract summary: This paper aims to adapt to specific fields at low cost using Unsupervised Domain Adaptation (UDA) We explore a novel domain shift from a diverse, large pool of internet-sourced data to a small set of data collected by a robot at specific locations. We introduce a novel module -- the Multi-level Attention-based Adrial Discriminator (MAAD) -- which can be integrated at the feature extractor level of any detection model.
• Score: 3.7619101673213664
• License:
• Abstract: In precision agriculture, vision models often struggle with new, unseen fields where crops and weeds have been influenced by external factors, resulting in compositions and appearances that differ from the learned distribution. This paper aims to adapt to specific fields at low cost using Unsupervised Domain Adaptation (UDA). We explore a novel domain shift from a diverse, large pool of internet-sourced data to a small set of data collected by a robot at specific locations, minimizing the need for extensive on-field data collection. Additionally, we introduce a novel module -- the Multi-level Attention-based Adversarial Discriminator (MAAD) -- which can be integrated at the feature extractor level of any detection model. In this study, we incorporate MAAD with CenterNet to simultaneously detect leaf, stem, and vein instances. Our results show significant performance improvements in the unlabeled target domain compared to baseline models, with a 7.5% increase in object detection accuracy and a 5.1% improvement in keypoint detection.

Related papers

• M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training [5.128670847334003]
  multimodal mobile sensing has been used extensively for inferences regarding health and well being, behavior, and context. The distribution of data in the training set differs from the distribution of data in the real world, the deployment environment. We propose M3BAT, an unsupervised domain adaptation for multimodal mobile sensing with multi-branch adversarial training.
  arXiv  Detail & Related papers  (2024-04-26T13:09:35Z)
• DODA: Diffusion for Object-detection Domain Adaptation in Agriculture [4.549305421261851]
  We propose DODA, a data synthesizer that can generate high-quality object detection data for new domains in agriculture. Specifically, we improve the controllability of layout-to-image through encoding layout as an image, thereby improving the quality of labels.
  arXiv  Detail & Related papers  (2024-03-27T08:16:33Z)
• Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer [69.82229895838577]
  Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains.
  arXiv  Detail & Related papers  (2023-11-21T13:12:21Z)
• Enhancing Visual Domain Adaptation with Source Preparation [5.287588907230967]
  Domain Adaptation techniques fail to consider the characteristics of the source domain itself. We propose Source Preparation (SP), a method to mitigate source domain biases. We show that SP enhances UDA across a range of visual domains, with improvements up to 40.64% in mIoU over baseline.
  arXiv  Detail & Related papers  (2023-06-16T18:56:44Z)
• Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data [55.41644538483948]
  Domain adaptation is a popular paradigm in modern machine learning. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) DAPCA finds a linear reduced data representation useful for solving the domain adaptation task.
  arXiv  Detail & Related papers  (2022-08-28T21:10:56Z)
• Unsupervised domain adaptation with non-stochastic missing data [0.6608945629704323]
  We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. Imputation is performed in a domain-invariant latent space and leverages indirect supervision from a complete source domain. We show the benefits of jointly performing adaptation, classification and imputation on datasets.
  arXiv  Detail & Related papers  (2021-09-16T06:37:07Z)
• Gradual Domain Adaptation via Self-Training of Auxiliary Models [50.63206102072175]
  Domain adaptation becomes more challenging with increasing gaps between source and target domains. We propose self-training of auxiliary models (AuxSelfTrain) that learns models for intermediate domains. Experiments on benchmark datasets of unsupervised and semi-supervised domain adaptation verify its efficacy.
  arXiv  Detail & Related papers  (2021-06-18T03:15:25Z)
• Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer [15.000304613769108]
  Techniques for multi-target domain adaptation (MTDA) seek to adapt a recognition model such that it can generalize well across multiple target domains. Key challenges include the lack of bounding box annotations for target data, knowledge corruption, and the growing resource requirements needed to train accurate deep detection models. We propose a new Incremental MTDA technique for object detection that can adapt a detector to multiple target domains, one at a time, without having to retain data of previously-learned target domains.
  arXiv  Detail & Related papers  (2021-04-13T19:35:54Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Domain Adaptation with Incomplete Target Domains [61.68950959231601]
  We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains.
  arXiv  Detail & Related papers  (2020-12-03T00:07:40Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.