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目标检测非常类似,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,实际上,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。 一 定义 OVD是在...
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...
DetPro模型是在论文“Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model”中被提出的模型,它的是“detection prompt”,意思就是说在检测任务中使用了prompt方法。 标题:Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model 机构:Tsinghua University, M...
for each object in the predefined objects, e.g., racket, use the grad-cam to visulize its activation map. apply proposal generator to get multiple boxes the box with the largest overlap with the activation map is regarded as the pseudo box. 2.2 open vocabulary object detector with pseudo-b...
Open-vocabulary detection (OVD) aims to generalize beyond the limited number of base classes labeled during the training phase. The goal is to detect novel classes defined by an unbounded (open) vocabulary at inference.Benchmarks Add a Result These leaderboards are used to track progress in ...
开放词集目标检测(Open Vocabulary Detection, OVD),亦即开放世界目标检测,提供了解决上述问题的新思路。借助于现有跨模态模型(CLIP[1]、ALIGN[2]、R2D2[3] 等)的泛化能力,OVD可以实现以下功能:1)对已定义类别的few shot检测;2)对未定义类别的zero-shot检测。OVD技术的出现吸引了计算机视觉研究者们的广泛关注,首先...
Open Vocabulary Detection Contest - 开放世界目标检测竞赛的官网链接:开放世界目标检测竞赛2023 (360cvgroup.github.io) 在各个竞赛团队的积极参与、中国图象图形学学会与360人工智能研究院的大力支持下,Open Vocabulary Detection Contest - 开放世界目标检测竞赛已经正式结束,在征集各个竞赛团队的许可后,我们将部分优胜...
导读由中国图象图形学学会与360人工智能研究院举办的Open Vocabulary Detection Contest - 开放世界目标检测竞赛已经正式结束,本文在征集各个竞赛团队的许可后,对部分优胜团队的技术方案汇总并公开分享。 分享嘉宾|王斌 360人工智能研究院 编辑整理|李同学 出品社区|DataFun ...
Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of ...
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 ...