两篇新研究论文提出了新颖的目标检测方法。VFM4SDG 旨在通过使用冻结的视觉基础模型来维持跨域稳定性，从而改进单域泛化目标检测，解决天气和光照变化的问题。UHR-DETR 通过有效分配计算资源并整合全局和局部场景信息，解决了超高分辨率遥感图像中小目标检测的挑战。 AI
排序理由 两篇新的 arXiv 论文提出了新颖的目标检测方法，属于研究类别。
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<!-- Part 4 of the "Object Detection for Dummies" series focuses on one-stage models for fast detection, including SSD, RetinaNet, and models in the YOLO family. These models skip the explicit region proposal stage but apply the detection directly on dense sampled areas. --> <p>I…
<!-- In Part 3, we would examine four object detection models: R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN. These models are highly related and the new versions show great speed improvement compared to the older ones. --> <p><span class="update">[Updated on 2018-12-20: Remove…
<!-- Part 2 introduces several classic convolutional neural work architecture designs for image classification (AlexNet, VGG, ResNet), as well as DPM (Deformable Parts Model) and Overfeat models for object recognition. --> <p><a href="https://lilianweng.github.io/posts/2017-10-29…
<!-- In this series of posts on "Object Detection for Dummies", we will go through several basic concepts, algorithms, and popular deep learning models for image processing and objection detection. Hopefully, it would be a good read for people with no experience in this field but…
In real-world scenarios, continual changes in weather, illumination, and imaging conditions cause significant domain shifts, leading detectors trained on a single source domain to degrade severely in unseen environments. Existing single-domain generalized object detection (SDGOD)…
Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small objects in such vast scenes presents a critical dilemma: retaining the original resolution for small objects causes prohibitive me…