Winoground: Probing vision and language models for visio-linguistic compositionality[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 5238-5248. ^Diwan A, Berry L, Choi E, et al. Why is winoground hard? investigating failures in visuolinguistic ...
2.1. Taxonomy of Vision-and-Language Models(视觉语言模型分类法) 2.2. Modality Interaction Schema(模态交互模式) 2.3. Visual Embedding Schema(视觉嵌入模式) 3. Vision-and-Language Transformer 3.1. Model Overview 3.2. Pre-training Objectives 3.3. Whole Word Masking(整个单词Mask) 3.4. Image Augmentation...
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...
Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next. However, unlike most language models, LaMDA was trained on dialogue to pick up on nuances ...
这些目标类别可能出现在训练图像中,但没有用地面实况边界框进行注释。利用未标记数据的常见且成功的方法是生成伪标签。然而,在SSOD的所有先前工作中,仅利用了少量标记数据生成伪标签,而在OVD的大部分先前工作中根本不利用伪标签。 在本文中,我们提出了一种简单而有效的方法,利用最近提出的视觉和语言(V&L)模型来...
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,可以有效地训练代...
Two recent surveys on pretrained language models Pre-trained Models for Natural Language Processing: A Survey, arXiv 2020/03 A Survey on Contextual Embeddings, arXiv 2020/03 Other surveys about multimodal research Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets,...
In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question ...
Fooling Vision and Language Models Despite Localization and Attention Mechanism Xiaojun Xu1,2, Xinyun Chen2, Chang Liu2, Anna Rohrbach2,3, Trevor Darrell2, Dawn Song2 1Shanghai Jiao Tong University, 2EECS, UC Berkeley, 3MPI for Informatics Abstract Adversarial attacks are known to succeed 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。