然后提出了GLIP模型:Grounded Language-Image Pre-training。 GLIP的主要贡献如下: 将phrase grounding和目标检测任务统一,将image和text prompt同时输入到目标检测网络中,prompt中带有图片中所有类别的详细描述。 GLIP采用了丰富的预训练数据,使得它的预训练模型可以更轻松地迁移到下游任务中。预训练的GL
GLIP-T (C) is pre-trained on 1) O365 and 2) GoldG, 0.8M human-annotated gold grounding data curated by MDETR [23], including Flickr30K, VG Caption [28], and GQA [19]. We have removed COCO images from the dataset. It is designed to verify the effectiveness of gold grounding dat...
这个任务是学习object-level,语言感知和语义丰富的视觉表示的有效和可扩展的预训练任务,并提出了Grounded Language-Image Pre-training(GLIP)。我们的方法统一了phrase grounding和object detection任务,object detection可以被转换为上下文无关的phrase grounding,而phrase grounding可以被视为置于context背景下的的object ...
GLIPv1: Grounded Language-Image Pre-training GLIPv2: Unifying Localization and VL Understanding 代码地址:https://github.com/microsoft/GLIP 论文地址1:https://paperswithcode.com/paper/grounded-language-image-pre-training 论文地址2:https://arxiv.org/abs/2206.05836 翻译1https://zhuanlan.zhihu.com/...
This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from ...
Grounded Language-Image Pre-training(GLIP)是一种基于语言和图像的预训练方法,旨在为下游自然语言处理(NLP)和计算机视觉(CV)任务提供有用的特征表示。该方法通过对大规模文本和图像数据进行联合学习,使得模型能够将视觉信息与语言知识相结合。 3.2 代码功能与实现原理 GLIP代码的主要功能是实现了语言和图像之间的联合...
Thus we reported two numbers on LVIS: the performance of the last checkpoint (LVIS[2]) and the performance of the best checkpoint during the pre-training course (LVIS[3]).[4] Zero-shot performance on the 13 ODinW datasets. The numbers reported in the GLIP paper is from the best ...
GLIP将目标检测与phrase grounding统一在同一个框架下，通过输入imagetext提示，显著提升了目标检测的精确度。创新的分类流程：GLIP的分类流程考虑了phrase的子词匹配，成功解决了多词类别识别中的难题，特别是在处理复杂语境时表现出色。LanguageAware Deep Fusion策略：GLIP采用早期融合图像和文本特征的策略，...
对于模型的输入,GLIP开天辟地地对目标检测任务进行了重新定义,作者认为,目标检测实际上可以重新定义,可以是做把任何一张训练中的image,其上出现的所有样本的标签在分散之后拼接成一句话,从而把目标检测任务重新转换伪短语定位任务。 通过这种方式,所有的目标检测数据集都可转化为短语定位数据集。然后通过对文字和图片分别...
GLIP (Grounded Language-Image Pre-training) is a generalizable object detection (we use object detection as the representative of localization tasks) model. As illustrated in Figure 1, it is language aware, taking a natural language prompt as instruction. It is also semantic...