RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition
Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks. However, most existing advances of deep learning methods in the RSI field heavily rely on the features extracted by the manually designed backbone network, which severely hinders the potential of deep learning models due the complexity of RSI and the limitation of prior knowledge. In this paper, we research a new design paradigm for the backbone architecture in RSI recognition tasks, including scene classification, land-cover classification and object detection. A novel one-shot architecture search framework based on weight-sharing strategy and evolutionary algorithm is proposed, called RSBNet, which consists of three stages: Firstly, a supernet constructed in a layer-wise search space is pretrained on a self-assembled large-scale RSI dataset based on an ensemble single-path training strategy. Next, the pre-trained supernet is equipped with different recognition heads through the switchable recognition module and respectively fine-tuned on the target dataset to obtain task-specific supernet. Finally, we search the optimal backbone architecture for different recognition tasks based on the evolutionary algorithm without any network training. Extensive experiments have been conducted on five benchmark datasets for different recognition tasks, the results show the effectiveness of the proposed search paradigm and demonstrate that the searched backbone is able to flexibly adapt different RSI recognition tasks and achieve impressive performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningLand Cover ClassificationNeural Architecture Searchobject-detectionObject DetectionScene ClassificationSimilar Papers 제목 키워드 기반
Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data …
Scene ClassificationFew-Shot LearningDifferentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures
This study aims at making the architecture search process more adaptive for one-shot or online training. It is extended from the existing study on differentiable neural architecture search, and we made the backbone archi…
Language ModelingLanguage ModellingNeural Architecture SearchTime Series+2Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection
Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically have a feature backbone to extract useful …
object-detectionObject DetectionReconstruction Guided Few-shot Network For Remote Sensing Image Classification
Few-shot remote sensing image classification is challenging due to limited labeled samples and high variability in land-cover types. We propose a reconstruction-guided few-shot network (RGFS-Net) that enhances generaliza…
Remote Sensing Image ClassificationImage ReconstructionZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search
In object detection models, the detection backbone consumes more than half of the overall inference cost. Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Arch…
GPUNeural Architecture SearchObjectobject-detection+1