# GLRT-Based Metric Learning for Remote Sensing Object Retrieval

arXiv: https://arxiv.org/abs/2410.05773 · HTML: https://mlsanity.com/paper/2410.05773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID](https://mlsanity.com/paper/2006.02713/) (2020)
- [Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification](https://mlsanity.com/paper/1711.07027/) (2017)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/1904.01990/) (2019)
- [AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2004.08787/) (2020)
- [A Discriminatively Learned CNN Embedding for Person Re-identification](https://mlsanity.com/paper/1611.05666/) (2016)
- [Unsupervised Domain Adaptive Re-Identification: Theory and Practice](https://mlsanity.com/paper/1807.11334/) (2018)
- [Self-training with progressive augmentation for unsupervised cross-domain person re-identification](https://mlsanity.com/paper/1907.13315/) (2019)
- [Unsupervised Person Re-identification by Soft Multilabel Learning](https://mlsanity.com/paper/1903.06325/) (2019)
- [Mining on Manifolds: Metric Learning without Labels](https://mlsanity.com/paper/1803.11095/) (2018)
- [Adaptive Sparse Pairwise Loss for Object Re-Identification](https://mlsanity.com/paper/2303.18247/) (2023)
- [Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID](https://mlsanity.com/paper/2003.06650/) (2020)
- [Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network](https://mlsanity.com/paper/1902.05818/) (2019)
- [Hashing with Mutual Information](https://mlsanity.com/paper/1803.00974/) (2018)
- [Center Contrastive Loss for Metric Learning](https://mlsanity.com/paper/2308.00458/) (2023)

## Abstract (arXiv)

With the improvement in the quantity and quality of remote sensing images, content-based remote sensing object retrieval (CBRSOR) has become an increasingly important topic. However, existing CBRSOR methods neglect the utilization of global statistical information during both training and test stages, which leads to the overfitting of neural networks to simple sample pairs of samples during training and suboptimal metric performance. Inspired by the Neyman-Pearson theorem, we propose a generalized likelihood ratio test-based metric learning (GLRTML) approach, which can estimate the relative difficulty of sample pairs by incorporating global data distribution information during training and test phases. This guides the network to focus more on difficult samples during the training process, thereby encourages the network to learn more discriminative feature embeddings. In addition, GLRT is a more effective than traditional metric space due to the utilization of global data distribution information. Accurately estimating the distribution of embeddings is critical for GLRTML. However, in real-world applications, there is often a distribution shift between the training and target domains, which diminishes the effectiveness of directly using the distribution estimated on training data. To address this issue, we propose the clustering pseudo-labels-based fast parameter adaptation (CPLFPA) method. CPLFPA efficiently estimates the distribution of embeddings in the target domain by clustering target domain instances and re-estimating the distribution parameters for GLRTML. We reorganize datasets for CBRSOR tasks based on fine-grained ship remote sensing image slices (FGSRSI-23) and military aircraft recognition (MAR20) datasets. Extensive experiments on these datasets demonstrate the effectiveness of our proposed GLRTML and CPLFPA.