arXiv:2608.29852v1 Announce Type: new Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-se...
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
arXiv:2608.29917v1 Announce Type: new Abstract: Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes...
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
arXiv:2608. 03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped.
OD3 introduces an optimization‑free dataset distillation framework tailored for object detection. The method first iteratively places object instances in synthesized images, then screens candidates with a pre‑trained observer model to discard low‑confidence objects. Applied to MS COCO and PASCAL VOC, OD3 achieves compression ratios from 0.25% to 5% and surpasses previous detection‑focused distillation methods by over 14% on COCO mAP50 at a 1.0% compression ratio.
Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes the object within a target image, the latter retr...
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.
arXiv:2512.07776v2 Announce Type: replace Abstract: Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals fr...
The paper demonstrates that object detection benchmarks suffer from incomplete annotations, with re-annotation of COCO, Pascal VOC, Cityscapes, and KITTI revealing up to a 60% increase in detected objects, especially small, occluded, or densely packed instances. The authors propose a scalable annotation pipeline that uses multiple annotators per object to capture uncertainty and improve recall, and they introduce two new large-scale benchmarks: an uncertainty-aware detection benchmark and a label error detection benchmark based on real errors. Their findings show that benchmark performance is highly sensitive to annotation quality, yet model rankings remain largely unchanged, highlighting the need for uncertainty-aware evaluation to better reflect real-world ambiguity.
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed.