Our method distills the knowledge from a pretrained open-vocabulary image classification model (teacher) into a two-stage detector (student). Specifically, we use the teacher model to encode category texts and image regions of object proposals. Then we train a student detector, whose region embedd...
open-vocabulary object detection (OVD)可以翻译为“面向开放词汇下的目标检测”,该任务和zero-shot object detection非常类似,核心思想都是在可见类(base class)的数据上进行训练,然后完成对不可见类(unseen/ target)数据的识别和检测,除了核心思想类似外,很多论文其实对二者也没有进行很好的区分。然而,在本文中,并...
CLIP关注的是一个封闭集问题，因此对于open-vocabulary问题并不能很好的适应。通过一个引理证明了通过一个...
如图所示,是本次比赛中提出的算法 pipeline,不需要使用提供的类别信息,不引入额外的数据,即可进行任意商品类别的目标检测: 前景检测器(Foreground Detector):不需要使用提供的233类类别信息,只使用位置坐标训练一个前景检测器,整个 pipeline 中只有这里进行梯度更新; 提示词工程(prompt engineering):使用大语言模型(LLM)...
2.2 open vocabulary object detector with pseudo-bounding box: because of the two-step pipeline, any OVD model is acceptable. in this paper, a typical framework for OVD is thus selected. image.png input: image,large-scale object vocabulary set ...
OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision xiaomoguhz/ov-dquo • • 28 May 2024 Open-Vocabulary Detection (OVD) aims to detect objects from novel categories beyond the base categories on which the detector is trained....
We assemble DetPro with ViLD, a recent state-of-the-art open-world object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the bas...
右边:应用到下游的时候把pretrain时候的GAP改成detector head; :(个人认为是论文中最为核心的部分就是在pretrain的阶段建立起region跟text之间的关系,具体的实现是通过CPE模块对positional embedding进行random 变换得到的,论文中其他的部分例如loss修改等细节就不再介绍了,大家感兴趣的可以再follow原文。
本工作的主要贡献是引入了Zero-shot Interactive Personalized Object Navigation (ZIPON),这是一个新型模型,其中机器人可以在与用户对话的同时导航至个人目标对象。论文还提出了一个名为Open-woRld Interactive persOnalized Navigation (ORION)的新框架,该框架利用大型语言模型来制定序列决策,操作用于感知、导航和通信的模...
Our method distills the knowledge from a pretrained open-vocabulary image classification model (teacher) into a two-stage detector (student). Specifically, we use the teacher model to encode category texts and image regions of object proposals. Then we train a student detector, whose region ...