在 grounded semantics 里面，grounding的意思是语言的符号到语义的连接，可以翻译成，“语义根基“。
Language Grounded Pretraining The goal of this stage is to anchor the representation space to the much more structured language-based CLIP space. For this, we first preprocess CLIP text encodings of ScanNet200 categories to save computation, then pretrain our models with our Contrastive loss formul...
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 ...
By letting the agents acquire a grounded semantics at the same time they jointly construct a shared communication language we allow them not only to communicate facts about their environment, but to understand as well the meanings of such facts in an intuitive way. This enables the agents to ...
01/17/2023: From image understanding to image generation for open-set grounding? Check outGLIGEN (Grounded Language-to-Image Generation) GLIGEN: (box, concept)→image || GLIP: image→(box, concept) 09/19/2022: GLIPv2 has been accepted to NeurIPS 2022 (Updated Version). ...
微软的《Grounded Language-Image Pre-training（GLIP）》文章提出了一种结合短语定位与目标检测的预训练方法，显著拓宽了自然语言在目标检测领域的应用。GLIP模型不仅在COCO、LVIS等任务中刷新了历史最好成绩，还展示了卓越的零样本预测能力。GLIP模型通过将目标检测任务转换为短语定位任务，利用语言-图像预...
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback http://t.cn/A6NXTAqG Nikhil Mehta, Milagro Teruel, Patricio Figueroa Sanz, ...
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 ...
GLIP：Grounded Language-Image Pre-training 当前视觉识别任务通常受限于预定义类别范围，限制了其在真实场景应用的扩展。CLIP的出现打破了这一限制，通过利用图文对进行训练，使模型能够根据文本提示识别任意类别，这在分类任务上表现优秀。GLIP则试图将这一技术应用于目标检测等复杂任务中，创新性地引入了...
探索视觉领域的革新，GLIP——Grounded Language-Image Pre-training，以突破性的技术引领我们进入一个全新的视觉识别时代。相较于传统的界限，CLIP和GLIP以image-text联合学习的方式，为各种任务带来了革命性的提升。其中，GLIP的独到之处在于其phrase grounding概念的引入，将目标检测与词义定位完美融合，...