From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationKeypoint Detectionobject-detectionObject DetectionUnsupervised Domain AdaptationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learnin…
Action RecognitionDeep LearningDomain AdaptationSurvey+1Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields
Depth estimation is a fundamental issue in 4-D light field processing and analysis. Although recent supervised learning-based light field depth estimation methods have significantly improved the accuracy and efficiency o…
Depth EstimationDepth PredictionUnsupervised Discovery and Composition of Object Light Fields
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised discovery of object-centric neural scene r…
Novel View SynthesisObjectScene UnderstandingSimple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images
High-quality labeled datasets are essential for deep learning. Traditional manual annotation methods are not only costly and inefficient but also pose challenges in specialized domains where expert knowledge is needed. S…
ClassificationDimensionality Reductionimage-classificationImage ClassificationUnsupervised Domain Adaptation: A Reality Check
Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baseline algorithms are not tested to the exten…
Domain AdaptationUnsupervised Domain Adaptation