← Back to all news
arXiv Computer Vision By Xianchi Dong, Yingyan Hou, Chao Ren, Wanxuan Lu, Zihan Wei, Hongfeng Yu, Yixiao Wang, Chubo Deng, Xian Sun

PDA++: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing

Read the original on arXiv Computer Vision →

PDA++ is a unified, environment‑aware object insertion framework for remote sensing imagery that improves few‑shot and long‑tail recognition. It operates in three stages: Planning, which selects scene‑compatible poses using an affordance field; Decoupling, which conditions the background on pose to preserve object identity while adapting to the scene; and Assimilation, which aligns multi‑scale texture distributions via optimal transport to enhance local coherence. The method achieves a whole‑image FID of 6.28 and boosts average few‑shot recognition mAP50 by 17.69 points on optical data, while also improving ship detection on SAR imagery and maintaining performance under cross‑dataset transfer and amorphous‑target insertion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 3

Domain shift-robust object detection with GenAI image editing

The paper investigates using diffusion-based generative image editing to improve object detector robustness against domain shifts, specifically camouflaged military vehicle detection. By synthetically adding foliage, netting, and multi‑spectral camouflage to training data with models such as Qwen Image Edit 2509 and Flux.2 Dev, the authors demonstrate significant mAP gains (up to +20.1 for foliage) over detectors trained on uncamouflaged data. LoRA fine‑tuning further boosts performance for the more challenging multi‑spectral camouflage.

By Isabel D. Stein, Thijs A. Eker, Sebastiaan P. Snel, Ella P. Fokkinga, Klamer Schutte, Luca Ambrogioni, Friso G. Heslinga
diffusioncomputer-visionfine-tuning
More like this →
arXiv Computer Vision
1d ago

Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs

The paper introduces the Wide-area Spatio-temporal Scene Understanding (WSTU) problem, which demands simultaneous wide-area coverage, per-target resolution, and temporal continuity—capabilities lacking in existing datasets. To address this, the authors present HARD, an ultra‑high‑resolution (12768×9564) UAV dataset annotated for object detection, multi‑object tracking, and scene‑level visual question answering. They also propose a latency‑aware metric, streaming‑HOTA (s‑HOTA), and show through baseline experiments that high resolution and processing latency significantly impact detection, tracking, and VQA performance, revealing gaps in current methods for WSTU.

By Yuhang Zhu, Meiyi Zhu, Yunkai Dang, Zhangnan Li, Yuxuan Wang, Wenbin Li, Hongbing Pan
llmscomputer-visionnlpmultimodal
More like this →