Hugging Face Trending Papers June 9, 2026

ZODS-RS -- Zero-training Oriented Detection & Segmentation for Remote Sensing

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Remote-sensing and UAV applications need models that generalize across platforms and viewpoints without task-specific training. Yet training-free pipelines often falter on oriented geometry, scale/rotation variation, and crowded ports or airfields, and rarely unify detection and segmentation.

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computer-vision

arXiv Computer Vision

Sep 23

Latent Commonality Expectation-Maximisation for Box-supervised Tree Crown Instance Segmentation

arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...

By Thomas Pitts, Kunqi Li, Bin Liang

computer-vision benchmarks

Hugging Face Trending Papers

Jul 27

LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection

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.

diffusion computer-vision efficiency

arXiv Computer Vision

Sep 1

Variable-Granularity Tokenization for High-Resolution Object Detection

arXiv:2608.28706v1 Announce Type: new Abstract: ViT detectors fix a uniform token grid before any learned stage. A native-resolution aerial detector must then choose between resolving few-pixel objec...

By Khayrul Islam

llms computer-vision nlp

arXiv Machine Learning

Aug 18

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.

By Steven Wallace, William D Harcourt, Richard Hann, Aiden Durrant, Somayajulu Sripada, Georgios Leontidis

computer-vision fine-tuning benchmarks safety

arXiv AI

Jun 30

MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

arXiv:2506. 12697v3 Announce Type: replace-cross Abstract: Small-object detection in Unmanned Aerial Vehicle (UAV) imagery requires preserving weak local evidence while using broader context to separate tiny foreground targets from cluttered backgrounds.

By Yuxiang Wang, Xuecheng Bai, Chuanzhi Xu, Ying Zhou, Weidong Cai

computer-vision

arXiv AI

Aug 19

Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition

The paper evaluates out‑of‑the‑box object detection models for automatic target detection and recognition (ATD/R) in military settings. Six YOLO variants and two DETR variants were benchmarked on a new military dataset featuring vehicles, occlusions, and small targets, with performance measured in mAP@0.5 and mAP@0.5:0.95 across air‑to‑ground and ground‑to‑ground perspectives. Findings show larger models and DETR-based approaches perform best, fine‑tuning on the VisDrone dataset improves air‑to‑ground and small‑object performance, yet all models still struggle with small targets in air‑to‑ground scenarios.

By Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann, Stefan Becker, Simon Bensberg, Niccol\`o Camarlinghi, H{\aa}vard R. Eiring, Alexander W. Johnsgaard, Tanel Liiv, Giuseppe Martino, Matteo Marturini, Matthias Rapp, Jan Erik van Woerden, Alexander Wolpert, Hugo J. Kuijf

agents computer-vision fine-tuning benchmarks