Research map / Detection and segmentation / Open-set and few-shot detection
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train Faster R-CNN to regress oriented bounding boxes instead of horizontal boxes | Faster R-CNN · DOTA OBB | mAP: 39.95 -> 54.13 | Helped |
| Train YOLOv2 on DOTA rather than UCAS-AOD | YOLOv2 · DOTA test | Avg. AP: 53.55 -> 60.51 | Helped |
| Train YOLOv2 on DOTA rather than UCAS-AOD | YOLOv2 · UCAS-AOD test | Avg. AP: 89.41 -> 76.15 | Hurt |
Closest papers by meaning
- Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community (AAAI 2025 · similarity 0.87)
- Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery (CVPR 2025 · similarity 0.86)
- Multiview Aerial Visual RECognition (MAVREC): Can Multi-view Improve Aerial Visual Perception? (CVPR 2024 · similarity 0.85)
- FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection (AAAI 2024 · similarity 0.85)
- See What We Cannot See: A Geo-guided Reasoning Benchmark for Object Counting under Adverse Earth Observation Conditions (CVPR 2026 · similarity 0.85)
- InstructSAM: A Training-free Framework for Instruction-Oriented Remote Sensing Object Recognition (NeurIPS 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.