Depth-Adaptive TransformerMaha ElbayadJiatao GuEdouard GraveMichael AuliInternational Conference on Learning Representations
In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of...
In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of...
我们从Vision Transformer ViT[5]中获得灵感,设计了带有transformers的AdaBins模块。由于我们的数据集较小,我们还使用了提议的transformer的更小版本,并在以下描述中将此transformer称为mini ViT或mViT。 Bin-widths:Transformer需要一个固定大小的向量序列作为输入,而输入的是Xd∈R(H×W×Cd)的解码特征张量。因此,通过...
我们的变压器是一个小型transformer编码器(见表)。详情为1),并输出输出嵌入序列x0∈RS×E。我们在第一个输出嵌入上使用了MLP头(我们还试验了一个版本,该版本有一个额外的特殊令牌作为第一个输入,但没有看到任何改进)。MLP头使用REU激活并输出N维矢量b‘。最后,我们将向量b‘归一化,使其总和为1,以获得bin宽度...
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(computed over all L Transformer layers) 最终的loss公式: 论文中取(lambda1, lambda2) = (1, 0.1) emb和hidden的loss权重是因为他们有相同的维度和相似的规模。 深度自适应 Adaptive Depth 训好了width-adaptive的模型之后,就可以训自适应深度的了。浅层BERT模型的优化其实比较成熟了,主要的技巧就是蒸馏。作...
To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a...
To specify, we employ the Transformer decoder to generate bins, novelly viewing it as a direct set-to-set prediction problem. We further integrate a multi-scale decoder structure to achieve a comprehensive understanding of spatial geometry information and estimate depth maps in a coarse-to-fine ...
Binformer主要由三个基本组件组成(见图2):像素级模块、transformer模块和深度估计模块。此外,我们提出了辅助场景分类和多尺度预测细化策略,以进一步提高模型性能。 给定输入的RGB图像,像素级模块首先提取图像特征,并将其解码为多尺度即时特征F和每像素表示fp。得益于具有跳跃连接的编码器-解码器框架,BinsFormer可以充分提...