1.介绍 在之前的 open-set object detection (OSOD) 中,除了检测识别已知物体外,还会检测一些未知类别的物体,但把所有未知的物体都归到 “未知类”。该论文提出的 Open-Set Object Detection and Discovery (OSODD),不仅可以检测未知物体,还可以挖掘它们潜在的类别。OSODD 采用了两阶检测方式,先对已知物体
在之前的 open-set object detection (OSOD) 中,除了检测识别已知物体外,还会检测一些未知类别的物体,但把所有未知的物体都归到 “未知类”。该论文提出的 Open-Set Object Detection and Discovery (OSODD),不仅可以检测未知物体,还可以挖掘它们潜在的类别。OSODD 采用了两阶检测方式,先对已知物体和未知物体进行...
Open-Set Object Detection 模型不仅需要识别图像中的已知目标对象(即已知类别),还需要能够识别出图像中不属于已知类别的未知目标对象,并拒绝将它们错误地检测为已知目标对象(reject unknown)。 Open-World Object Detection 识别未知对象并标记为“unknown”(Open-Set);增量学习未知类别(Incrementally learn identified unkn...
In this paper, we consider a more practical yet challenging problem, Open-Set Semi-Supervised Object Detection (OSSOD). We first find the existing SSOD method obtains a lower performance gain in open-set conditions, and this is caused by the semantic expansion, where the distracting OOD ...
DINO-X: The World's Top-Performing Vision Model for Open-World Object Detection and Understanding pose-estimationopen-set-object-detectionvisual-promptregion-captionopen-set-object-segmentation UpdatedApr 21, 2025 Python [CVPR 2023] Official Pytorch code for PROB: Probabilistic Objectness for Open Worl...
This article reviews the advancements presented in the paper “Grounding DINO 1.5: Advance the ‘Edge’ of Open-Set Object Detection.” We will explore the metho…
ros2 run hobot_dosod hobot_dosod --ros-args -p feed_type:=1 --ros-args --log-level warn # Run mode 3: Use shared memory communication method (topic name: /hbmem_img) to perform inference in asynchronous mode and set the log level to warn: ros2 run hobot_dosod hobot_dosod --ros...
半监督目标检测(Semi-supervised Object Detection,以下简称SSOD)旨在利用大量的无标注数据来提高模型的检测能力。然而,一般的SSOD方法都假设无标注数据不包含分布外 (Out-of-distribution, OOD) 的类别,这对于大规模的无标注数据集是不现实的。因此,作者提出了一种更符合实际的研究问题——开集半监督目标检测(Open-...
While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, their generalization to CD-FSOD remains unclear: 1) can such open-set detection methods easily generalize to CD-FSOD? 2) If not, how can models be enhanced when facing huge ...
phase, effectively bridging the gap between closed-set and open-set detection. Compared to the baseline YOLO-World, the proposed DOSOD significantly enhances real-time performance while maintaining comparable accuracy. The slight DOSOD-S model achieves a Fixed AP of26.7%, compared to26.2%for YOLO-...