\quad 开放集目标检测(Open-Set Object Detection): OV-DETR和ViLD:OV-DETR和ViLD分别利用CLIP模型的图像和文本嵌入作为查询,在DETR框架中解码特定类别的盒子;ViLD则是从CLIP教师模型中提取知识到R-CNN式检测器中,以便学习的区域嵌入包含语言的语义。 GLIP的贡献:GLIP将对象检测定义为地面问题,利用额外的地面数据帮...
paper: Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection code: github.com/IDEA-Researc Abstract 在本文中,我们提出了一种开放集对象检测器,称为Grounding DINO,通过将基于Transformer的检测器DINO与真值预训练相结合,该检测器可以通过人类输入(如类别名称或指代表达)对任意物...
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 better simulates the real world compared with close-set object detection. Besides the classes of interest, it also pays attention to unknown objects in the environment. We extend the previous concept of open-set object detection, aiming to detect both known and unknown ...
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...
cornerstone of advancements in computer vision. Creating versatile and efficient detectors has been a significant focus on building real-world applications. The introduction of Grounding DINO 1.5 by IDEA Research marks a significant leap forward in this field, particularly in open-set object detection....
Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper propose...
Paper tables with annotated results for Open-Set Object Detection By Aligning Known Class Representations
The DOSOD package is an example of quantized deployment based on Decoupled Open-Set Object Detector. The image data comes from local image feedback and subscribed image messages. Additionally, DOSOD supports custom detection categories detect, which is the biggest difference with conventional detector....
In this paper, we develop an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring...