Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images
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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.
Key facts. Cited by 1 paper 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
32 of 47 references.
References · 32
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
- 2023Mistral 7Bcited by 3,164
- 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
- 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
- 2020Detection and Tracking Meet Drones Challengecited by 263
- 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 208
- 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
- 2023DetGPT: Detect What You Need via Reasoningcited by 112
- 2023CLIP the Gap: A Single Domain Generalization Approach for Object Detectioncited by 87
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- 2021TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarioscited by 83
- 2023Dense Distinct Query for End-to-End Object Detectioncited by 77
- 2023Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Imagescited by 41
- 2024LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptorscited by 40
- 2021UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagerycited by 34
- 2024Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Modelscited by 30
- 2023Lenna: Language Enhanced Reasoning Detection Assistantcited by 29
- 2024Generative Region-Language Pretraining for Open-Ended Object Detectioncited by 29
- 2019Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approachcited by 23
- 2020Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detectioncited by 20
- 2022Focus-and-Detect: A Small Object Detection Framework for Aerial Imagescited by 19
- 2023Learning Domain-Aware Detection Head with Prompt Tuningcited by 18
- 2019Guided Attention Network for Object Detection and Counting on Dronescited by 14
- 2020SpotNet: Self-Attention Multi-Task Network for Object Detectioncited by 14
- 2024YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Imagescited by 11
- 2023FLSL: Feature-level Self-supervised Learningcited by 9
- 2024Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videoscited by 9
- 2023OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Imagescited by 8
- 2023Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagerycited by 5
- 2021Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learnercited by 2
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