# Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

arXiv: https://arxiv.org/abs/1712.03323 · HTML: https://mlsanity.com/paper/1712.03323/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 17 of 17)

- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2103.05569/) (2021)
- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Towards Zero-shot Sign Language Recognition](https://mlsanity.com/paper/2201.05914/) (2022)
- [Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors](https://mlsanity.com/paper/2407.10330/) (2024)
- [Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects](https://mlsanity.com/paper/2401.17766/) (2024)
- [SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting](https://mlsanity.com/paper/2412.08536/) (2024)
- [ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment](https://mlsanity.com/paper/2407.00891/) (2024)
- [On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey](https://mlsanity.com/paper/2408.04879/) (2024)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2402.02094/) (2024)
- [A Vision Centric Remote Sensing Benchmark](https://mlsanity.com/paper/2503.15816/) (2025)
- [Weakly Supervised Instance Attention for Multisource Fine-Grained Object Recognition with an Application to Tree Species Classification](https://mlsanity.com/paper/2105.10983/) (2021)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing](https://mlsanity.com/paper/2504.16433/) (2025)
- [Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition](https://mlsanity.com/paper/2509.07495/) (2025)

## Abstract (arXiv)

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.