# Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images

arXiv: https://arxiv.org/abs/2505.23193 · HTML: https://mlsanity.com/paper/2505.23193/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 30 of 32)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection](https://mlsanity.com/paper/2209.09407/) (2022)
- [DetGPT: Detect What You Need via Reasoning](https://mlsanity.com/paper/2305.14167/) (2023)
- [CLIP the Gap: A Single Domain Generalization Approach for Object Detection](https://mlsanity.com/paper/2301.05499/) (2023)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [Dense Distinct Query for End-to-End Object Detection](https://mlsanity.com/paper/2303.12776/) (2023)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors](https://mlsanity.com/paper/2402.04630/) (2024)
- [UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery](https://mlsanity.com/paper/2112.10415/) (2021)
- [Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models](https://mlsanity.com/paper/2405.15574/) (2024)
- [Lenna: Language Enhanced Reasoning Detection Assistant](https://mlsanity.com/paper/2312.02433/) (2023)
- [Generative Region-Language Pretraining for Open-Ended Object Detection](https://mlsanity.com/paper/2403.10191/) (2024)
- [Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach](https://mlsanity.com/paper/1908.03856/) (2019)
- [Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection](https://mlsanity.com/paper/2011.03822/) (2020)
- [Focus-and-Detect: A Small Object Detection Framework for Aerial Images](https://mlsanity.com/paper/2203.12976/) (2022)
- [Learning Domain-Aware Detection Head with Prompt Tuning](https://mlsanity.com/paper/2306.05718/) (2023)
- [Guided Attention Network for Object Detection and Counting on Drones](https://mlsanity.com/paper/1909.11307/) (2019)
- [SpotNet: Self-Attention Multi-Task Network for Object Detection](https://mlsanity.com/paper/2002.05540/) (2020)
- [YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images](https://mlsanity.com/paper/2404.06180/) (2024)
- [FLSL: Feature-level Self-supervised Learning](https://mlsanity.com/paper/2306.06203/) (2023)
- [Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos](https://mlsanity.com/paper/2401.03522/) (2024)
- [OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images](https://mlsanity.com/paper/2304.11805/) (2023)

## Built on by (most cited first, 1 of 1)

- [Robust Grounding with MLLMs Against Occlusion and Small Objects via Language-Guided Semantic Cues](https://mlsanity.com/paper/2604.24036/) (2026)

## Abstract (arXiv)

Despite recent advancements in computer vision research, object detection in aerial images still suffers from several challenges. One primary challenge to be mitigated is the presence of multiple types of variation in aerial images, for example, illumination and viewpoint changes. These variations result in highly diverse image scenes and drastic alterations in object appearance, so that it becomes more complicated to localize objects from the whole image scene and recognize their categories. To address this problem, in this paper, we introduce a novel object detection framework in aerial images, named LANGuage-guided Object detection (LANGO). Upon the proposed language-guided learning, the proposed framework is designed to alleviate the impacts from both scene and instance-level variations. First, we are motivated by the way humans understand the semantics of scenes while perceiving environmental factors in the scenes (e.g., weather). Therefore, we design a visual semantic reasoner that comprehends visual semantics of image scenes by interpreting conditions where the given images were captured. Second, we devise a training objective, named relation learning loss, to deal with instance-level variations, such as viewpoint angle and scale changes. This training objective aims to learn relations in language representations of object categories, with the help of the robust characteristics against such variations. Through extensive experiments, we demonstrate the effectiveness of the proposed method, and our method obtains noticeable detection performance improvements.