LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.
By Yupeng Zhang, Fangzhuo Gao, Juntao Cheng, Ziyi Zhao, Liang Wan, Ruize Han
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
Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference.
arXiv:2512. 18046v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems.
By Ami Pandat, Punna Rajasekhar, Gopika Vinod, Rohit Shukla
arXiv:2610.33430v1 Announce Type: new
Abstract: Recent DETR-based tiny object detectors adopt dynamic query mechanisms to handle severe density imbalance. However, existing designs entangle two effec...
By Yi Zhang, Di Xiong, Ge Gao, Xiangyue Zhang, Yihang Qiu, Yuxuan Zhou, Shuo Chen
arXiv:2608.23923v1 Announce Type: new
Abstract: Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles...
By Rashid Riyadh, Abd Ullah Khan, Imad Gohar, Muzammil Behzad
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel
arXiv:2611.07994v1 Announce Type: new
Abstract: Tiny object detection remains challenging because tiny instances occupy only a few pixels, and their fine-grained details are easily lost during featur...
By Zehao Wang, Si Wu, Guiping Cao, Xin Li, Yaowei Wang
BiHashFormer is a dual‑branch transformer that uses hashing to efficiently detect objects in high‑resolution wide (HRW) shots. It employs a RoI Selector to pick target‑containing windows, a compression branch that refines attention via HashAttention by discarding half the key‑value pairs, and a compensation branch that recovers missed objects with HashMiner. Experiments on the PANDA gigapixel benchmark show a 37.5% reduction in backbone FLOPs and an AP50 of 81.0%, while the parallel structure avoids inference delays and improves stability compared to single‑branch models.
By Jingchen Huang, Wenxi Li, Chenyang Lyu, Moran Liu, Haozhe Lin, Yuchen Guo
arXiv:2608.30618v1 Announce Type: new
Abstract: Transformer-based decoders for 3D instance segmentation typically commit to a fixed number of queries and positional modeling calibrated on the trainin...
By Keno Moenck, Thorsten Sch\"uppstuhl
arXiv:2603.08898v3 Announce Type: replace
Abstract: In this paper, we introduce visual query segmentation (VQS), a new paradigm of visual query localization (VQL) that aims to segment all pixel-level...
By Bing Fan, Minghao Li, Hanzhi Zhang, Shaohua Dong, Naga Prudhvi Mareedu, Xiaoqiong Liu, Weishi Shi, Yunhe Feng, Yan Huang, Heng Fan
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.