Empowering lightweight detectors: Orientation Distillation via anti-ambiguous spatial transformation for remote sensing images
Abstract Knowledge distillation (KD) has been one of the most potential methods to
implement a lightweight detector, which plays a significant role in satellite in-orbit processing …
implement a lightweight detector, which plays a significant role in satellite in-orbit processing …
Directional Alignment Instance Knowledge Distillation for Arbitrary-Oriented Object Detection
A Wang, H Wang, Z Huang, B Zhao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently, many lightweight neural networks have been deployed on airborne or satellite
remote sensing platforms for real-time object detection. To bridge the performance gap …
remote sensing platforms for real-time object detection. To bridge the performance gap …
HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images
Height estimation has long been a pivotal topic within measurement and remote sensing
disciplines, with monocular height estimation offering wide-ranging data sources and …
disciplines, with monocular height estimation offering wide-ranging data sources and …
CapMatch: Semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition
This article proposes a semi-supervised contrastive capsule transformer method with feature-
based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) …
based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) …
Negative-Core Sample Knowledge Distillation for Oriented Object Detection in Remote Sensing Image
W Zhang, Y Zhang, F Huang, X Qi… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Knowledge distillation has been one of the most effective methods for enhancing the
performance of lightweight detectors, crucial for remote sensing edge intelligence models …
performance of lightweight detectors, crucial for remote sensing edge intelligence models …
STONet-S*: A Knowledge-Distilled Approach for Semantic Segmentation in Remote-Sensing Images
W Zhou, P Yang, W Qiu, F Qiang - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Semantic segmentation of remote sensing images is a critical research domain. The
integration of cross-modal features enhances stability in intricate environments. Despite the …
integration of cross-modal features enhances stability in intricate environments. Despite the …
in Detecting Cropping Intensity: An Attempt Based on Global Typical Samples
X Liu, H Shi, Y Zhang, Y Hou - Machine Learning and Intelligent …, 2024 - books.google.com
Accurate estimation of cropping intensity is crucial for agriculture pro-duction, land
management, and food security. Traditional land surveys and remote sensing techniques …
management, and food security. Traditional land surveys and remote sensing techniques …
Application of Convolutional Neural Networks in Detecting Cropping Intensity: An Attempt Based on Global Typical Samples
X Liu, H Shi, Y Zhang, Y Hou, L Niu, E Zhu, J Jia… - … Conference on Machine …, 2023 - Springer
Accurate estimation of cropping intensity is crucial for agriculture production, land
management, and food security. Traditional land surveys and remote sensing techniques …
management, and food security. Traditional land surveys and remote sensing techniques …
Local-to-Global Point Supervised Object Detector via Aggregation of Discriminative Parts
Advanced fully supervised detectors benefit from abundant bounding-box annotations which
accurately cover multi-scale objects. However, for point supervised object detectors (PSOD) …
accurately cover multi-scale objects. However, for point supervised object detectors (PSOD) …