DeiT: 为了缓解ViT对大数据集的依赖,Touvron等人提出了一种高效数据图像转换器(DeiT)[38],以提高其在ImageNet-1k上训练时的适用性。DeiT-B基于ViT-B[27],利用现有的数据增强和正则化策略,在ImageNet上获得了83.1%的top-1准确率。此外,teacher-student 策略应用于预训练,这是一个形式上类似于类token的蒸馏token,...
之后,DETR将自注意力成功应用到了物体检测任务上。计算机视觉中的子任务有很多,除了图像分类,物体检测,还有语义分割,深度估计,3D重建等等。研究自注意力在每个子任务上的适用性当然是有意义的,但是一个更值得研究的方向则是采用自注意力来提取适用于多种任务的通用的视觉特征。这其实与CNN中采用大规模数据集(比如Ima...
size(0)]: raise RuntimeError('The size of the 3D attn_mask is not correct.') # 现在 atten_mask 的维度就变成了3D 接着,在上述代码中第5-6行所完成的就是图3-7中的缩放过程;第8-16行用来判断或修改attn_mask的维度,当然这几行代码只会在解码器中的Masked Multi-Head Attention中用到。 # 第...
CIFAR100与ImageNet有一些相似之处,因此我们不会以任何形式重新训练ResNet模型。然而,如果你想获得最佳性能并且拥有一个非常大的数据集,最好在训练期间将ResNet添加到计算图中并微调其参数。由于我们没有足够大的数据集,并且希望高效地训练我们的模型,我们将事先提取特征。让我们在下面加载并准备模型。 import os #...
RuntimeError: the batch number of src and tgt must be equal I'm encountering a RuntimeError while training a Transformer model in PyTorch, specifically when trying to pass the source and target tensors to the model. The error message states: Here is the code: ... ...
在三个3D医学图像分割数据集 (BraTS2019、BraTS2020和BTCV)上的大量实验结果表明,MSCATU-Net 在脑肿瘤和腹部器官分割任务中有着良好的分割性能。 关键词:脑肿瘤MRI图像;Transformer;卷积神经网络;医学图像分割 II Abstract Abstract Magneticresonanceimaging(MRI)imagehasbecomeanimportantimaging methodforbraindisease...
P Zhang,L Dong,LW Zhang - Journal of Real-Time Image Processing 被引量: 0发表: 2024年 Human action recognition using an optical flow-gated recurrent neural network Recognizing various human actions in videos is considered a highly complicated problem, which has many potential applications in solvi...
Full size image First, we confirmed that Transformer embeddings and transformations outperform classical linguistic features in most language ROIs (p <0.005 in HG, PostTemp, AntTemp, AngG, IFG, IFGorb, vmPFC, dmPFC, and PMC for both embeddings and transformations; permutation test; FDR corrected...
It is an interesting attempt to combine the traditional image features with the transformer network. For proving the performance of the proposed method, the large scale point cloud benchmark Oakland 3D is utilized. In the experiments, the proposed method achieved 98.1% accuracy on the Oakland 3D ...
With the success of 2D CNNs in mind, some methods [26,27] use multi-view projections in which 3D point cloud is projected into multiple image planes. Then, in order to generate the final output representations, 2D CNNs are employed to extract feature representations in these image planes, ...