# Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioning

arXiv: https://arxiv.org/abs/2603.12832 · HTML: https://mlsanity.com/paper/2603.12832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Learning to Describe Differences Between Pairs of Similar Images](https://mlsanity.com/paper/1808.10584/) (2018)
- [Aerial Vision-and-Dialog Navigation](https://mlsanity.com/paper/2205.12219/) (2022)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning](https://mlsanity.com/paper/2009.14352/) (2020)
- [SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications](https://mlsanity.com/paper/1907.11093/) (2019)
- [Self-supervised Cross-view Representation Reconstruction for Change Captioning](https://mlsanity.com/paper/2309.16283/) (2023)
- [Altogether: Image Captioning via Re-aligning Alt-text](https://mlsanity.com/paper/2410.17251/) (2024)
- [Context-aware Difference Distilling for Multi-change Captioning](https://mlsanity.com/paper/2405.20810/) (2024)
- [MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization](https://mlsanity.com/paper/2305.08117/) (2023)
- [FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes](https://mlsanity.com/paper/2309.13882/) (2023)
- [Move and Act: Enhanced Object Manipulation and Background Integrity for Image Editing](https://mlsanity.com/paper/2407.17847/) (2024)
- [Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection](https://mlsanity.com/paper/2401.13995/) (2024)

## Abstract (arXiv)

This paper proposes a novel task for UAV scene understanding - UAV Scene Change Captioning (UAV-SCC) - which aims to generate natural language descriptions of semantic changes in dynamic aerial imagery captured from a movable viewpoint. Unlike traditional change captioning that mainly describes differences between image pairs captured from a fixed camera viewpoint over time, UAV scene change captioning focuses on image-pair differences resulting from both temporal and spatial scene variations dynamically captured by a moving camera. The key challenge lies in understanding viewpoint-induced scene changes from UAV image pairs that share only partially overlapping scene content due to viewpoint shifts caused by camera rotation, while effectively exploiting the relative orientation between the two images. To this end, we propose a Hierarchical Dual-Change Collaborative Learning (HDC-CL) method for UAV scene change captioning. In particular, a novel transformer, i.e. Dynamic Adaptive Layout Transformer (DALT) is designed to adaptively model diverse spatial layouts of the image pair, where the interrelated features derived from the overlapping and non-overlapping regions are learned within the flexible and unified encoding layer. Furthermore, we propose a Hierarchical Cross-modal Orientation Consistency Calibration (HCM-OCC) method to enhance the model's sensitivity to viewpoint shift directions, enabling more accurate change captioning. To facilitate in-depth research on this task, we construct a new benchmark dataset, named UAV-SCC dataset, for UAV scene change captioning. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on this task. The dataset and code will be publicly released upon acceptance of this paper.