Geoffrey:是的,这确实带来了一些显著的改进,但远远不及Transformers。Transformers在自然语言处理方面带来了巨大的改进。 李飞飞:神经架构搜索主要用于ImageNet。 Jordan:我来说一下我们对Transformers的经历。当时我们正在做Layer 6这家公司,我记得我们提前看过那篇论文的预印本。当时我们正处于融资和收购要约的过程中,读...
此外作者还提出了 CaiT,即 Class-Attention in Image Transformers,结构可参考下图: 最左为传统 Transformer 形式,最右侧为本文提出的,在前期不加入类别 token,而加入之后采用本文提出的 Class-Attention,先看定义: 之后Attention 操作不变,其实就是现在只获取类别 token 和其他 token 的关联就行了,不需要再更新特征 ...
[1] Masked Autoencoders Are Scalable Vision Learners [2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [3] BEIT: BERT Pre-Training of Image Transformers [4] Generative Pretraining from Pixels [5] Extracting and composing robust features with denoising autoencoders...
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation. deep-learningcnnpytorchsegmentationsemantic-segmentationremote-sensing-imagepytorch-...
pythonmachine-learningcomputer-visiondeep-learningpaperimage-processingtransformerstransformerobject-detectionimage-segmentationvisual-trackingsemantic-segmentationcvprcvpr2020cvpr2021cvpr2022cvpr2023cvpr2024cvpr2025 UpdatedFeb 28, 2025 albumentations-team/albumentations ...
Transformers for vision: When we began working on super-resolution, we saw that most approaches were still using CNN architectures. Given the Microsoft Turing team’sexpertiseandsuccessapplying transformers in large, language models and our recent use of transformers in ourmulti-modal Turing Bletchley...
64-An Empirical Study of Training Self-Supervised Vision Transformers MoCov1通过dictionary as a queue和momentum encoder和shuffle BN三个巧妙设计,使得能够不断增加K的数量,将Self-Supervised的威力发挥的淋漓尽致。MoCov2在MoCov1的基础上,增加了SimCLR实验成功的tricks,然后反超SimCLR重新成为当时的SOTA,FAIR和Goo...
convolution-free transformers teacher-student transformers 研究问题: 使用师生模型蒸馏 把大型transformer变成小型且准确度高的模型 研究原因: 大模型的vision transformer 显示了使用 研究设计: 只用Imagenet训练,单机不多于三天。86M参数的模型 研究结论: DeiT,它是一种不需要大量数据进行训练的图像转换器,得益于改进的...
近日,谷歌大脑团队公布了Vision Transformer(ViT)进阶版ViT-G/14,参数高达20亿的CV模型,经过30亿张图片的训练,刷新了ImageNet上最高准确率记录——90.45%,此前的ViT取得的最高准确率记录是 88.36%,不仅如此,ViT-G/14还超过之前谷歌提出的Meta Pseduo ...
Exploring Practical Deep Learning Approaches for English-to-Hindi Image Caption Translation Using Transformers and Object Detectorsdoi:10.1007/978-981-19-4831-2_5Most of the captions available for images are only present in a few languages prominent on the internet. The task of machine translation ...