前言 自从ChatGPT问世以来,人工智能领域经历了一场令人眼花缭乱的变革,特别是在视觉-语言模型(Vision-Language Models, VLMs)的研究和应用上更是如此。VLMs通过结合视觉感知能力和自然语言理解能力,已经在诸如图像描述、视觉问答以及图像和视频的自动标注等多个方面展示出其惊人的潜力和应用价值。随着技术的不断进步,VL...
Sound Symbolism in Vision-and-Language Modelsopenreview.net/forum?id=bfmSc1ETT9 作者:Morris Alper, Hadar Averbuch-Elor Affiliation:Tel Aviv University NeurIPS 2023 spotlight 本文是在 VLM 中挖掘一种名为 sound symbolism 的现象,这种现象讨论的是单词的读音是否和比表达的意思有关联。这一现象最有名...
VinVL: Revisiting visual representations in vision-language models(CVPR 2021)模型的核心backbone基于上面提到的Oscar架构,主要是对object detection部分进行了优化,核心是希望在图像侧能够通过OD识别出更多样的图像实体,得到更多的object tag和region feature,进而提升后续Oscar图文模型效果。本文的目标检测采用了C4模型,预...
How Are Vision Language Models Used? VLMs are quickly becoming the go-to tool for all types of vision-related tasks due to their flexibility and natural language understanding. VLMs can be easily instructed to perform a wide variety of tasks through natural language: Visual questions-answering...
We'll delve into the latest advancements in the world of large-scale language and vision language models, exploring enhancements introduced by each model, their capabilities, and potential applications.
Vision language models (VLMs) are AI models that can understand and process both visual and textual data, enabling tasks like image captioning, visual question answering, and text-to-image generation.
摘要论文Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning 的阅读。本文提出了一种名为VLM-RM的方法,使用预训练的视觉-语言模型(如CLIP)作为强化学习任务的奖励模型,以自然语言描述任务并避免手动设计奖励函数或收集昂贵的数据来学习奖励模型。实验结果显示,通过使用 VLM-RM,可以有效地训...
Vision language models (VLMs) are a type of artificial intelligence (AI) model that can understand and generate text about images. They do this by combining computer vision and natural language processing models. VLMs can take image inputs and generate text outputs. They can, for example, be...
Utilizing a contrastive vision-language model and a pre-trained large language model, BrainSCUBA generates interpretable captions, enabling text-conditioned image synthesis. This method shows that the generated images are semantically coherent and achieve high predicted activations. In exploratory studies on...
所以,注意力模块在产生 attention-pooled features 的时候,是依赖于其他模态的 --- 后续的 mimics common atteniton mechansims 是从其他 vision-and-language models 上得到的。剩下的 transformer 模块像之前一样,包含一个残差。总体来说,co-attention 对于 vision-and-language 来说并不是一个新的 idea。