vision transformer 虽然之前ViT证明在Transformer在视觉任务上的可能性。但其就是将原始图像分成一个一个patch,分别进行linear projection之后送入Transformer进行学习。这种方案token数保持不变,但是embedding的维度又不能太小(很难训练的很深)。而经过一系列实验证明 patch越小,得到的效果越好。作者希望能够利用更多的patc...
1 Introduction 目前缺少统一的多尺度encoder-decoder的结构,用于视频任务,即以往的方法只考虑了encoder或者decoder。为此,本文提出了第一个Multiscale Encoder Decoder Video Transformer (MED-VT)。在encoding时,它包含了尺度内和跨尺度attention,使得其能同时捕捉时空信息。在decoding时,它介绍了可学习的coarse-to-fine ...
The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET .This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations ...
We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then ...
We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then ...
We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension...
AI News Sharing Facebook AI has built Multiscale Vision Transformers (MViT), a Transformer architecture for representation learning from visual data such as images and videos. It’s a family of visual recognition models that incorporate the seminal concept of hierarchical representations into the ...
Typical multiscale transformer decoders for segmentation tasks learn a compressed representation, their queries, through information exchange across scales. Unlike previous work, we instead preserve the detailed feature maps during across scale information exchange via a multiscale memory transformer decoding...
Besides, in order to obtain more effective feature representations, an efficient multi-scale visual transformer is introduced for feature encoder. More importantly, we employ a weighted loss function composed of focal, multiscale structure similarity and Jaccard index to penalize the training error of ...
We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram, we first patchify and project it into an initial temporal resolution and embeddin...