GLIP-T is based on the Swin-Tiny backbone and pretrained on the following data: 1) O365, 2) GoldG as in GLIP-T (C), and 3) Cap4M, 4M image-text pairs collected from the web with boxes generated by GLIP-T (C) 使用self-training;由上面描述,teacher模型是GLIP-T (C) self-train...
Since diffusion models have been trained on billions of image-text pairs [47], a natural question is: Can we build upon existing pretrained diffusion models and endow them with new conditional input modalities? In this way, analogous to the recognition literature, we may be able to achi...
Since diffusion models have been trained on billions of image-text pairs [53], a natural question is: Can we build upon existing pretrained diffusion models and endow them with new conditional input modalities? In this way, analogous to the recognition literature, we may be able to achieve ...
The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned ...
Grounded Language-Image Pre-training 基础语言-图像预训练Paper: Grounded Language-Image Pre-training (arxiv.org)Github: https://github.com/microsoft/GLIP本篇 Grounded Language-Image Pre-training:首…
PitVQA comprises 25 procedural videos and a rich collection of question-answer pairs spanning crucial surgical aspects such as phase and step recognition, context understanding, tool detection and localization, and tool-tissue interactions. PitVQA-Net consists of a novel image-grounded text embedding ...
This reformulation allows us to pre-train GLIP onscalableandsemantic-richdata: millions of image-caption pairs with millions of unique grounded phrases. Given a good grounding model (a teacher GLIP trained on a moderate amount of gold grounding data), we ca...
where ΓAFFrepresents the AFF operation;βrepresents the upsampling operation;Vsrepresents input image features. To derive more meaningful multimodal knowledge prompts from the visual and textual alignment features, the learned multimodal prompts are incrementally integrated into the text feature space using...
After removing duplicate pairs of phrases, we ask workers to annotate about 11k samples in the test and validation sets as well as 11k samples in the training set. During the annotation process, we show workers the VGP pair, the image5 and original captions which the phrases belong to.6 ...
To address the lack of domain-specific datasets, we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. Geo...