In this paper, we propose a gated cross-attention network for universal speaker extraction. In our model, the cross-attention mechanism learns the correlation between the target speaker and the speech to determine whether the target speaker is present. Based on this correlation,...
In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extracto...
62 - Day 5 CrossValidation and Model Evaluation Techniques 13:01 63 - Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearc 19:29 64 - Day 7 Optimization Project Building and Tuning a Final Model 22:46 65 - Introduction to Week 9 Neural Networks and Deep Learning...
Medical Transformer (MedT) uses gated axial attention layer as the basic building block and uses LoGo strategy for training. MedT has two branches - a global branch and local branch. The input to both of these branches are the feature maps extracted from an initial conv block. This block h...
KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module, integrated within a UNet++ ... Z Zhao,S Faghihroohi,Y Zhao,... 被引量: 0发表...
Gated Axial-Attention 而axial-attention是在大量的数据下训练的,当在小规模的数据集上(医学数据)在学习到的相对位置编可能不精确,在不够精确的情况下,将它们分别添加到相应的键、查询和值张量中会导致性能下降。为此本文提出了改进的axial-attention,可以控制位置信息对非本局部下文的编码的影响。
一般的self-attention 是用的dot形式 2.4 Cross Attention Multimodal 作者想使得两个模态序列之间进行交互,获取他们之间的交互信息,于是使用 cross-modal attention(非常常见的 QKV 不同模态即可实现) 我们计算 T(text) 对 V(video)模态的注意力 然后乘以 V 模态 就得到 T对V 的交互信息 (下图的Ctv) ...
To this end, we propose a gated position-sensitive axial attention mechanism where we introduce four gates that control the amount of information the positional embedding supply to key, query, and value. These gates are learnable parameters which make the proposed mechanism to be applied to any ...
Furthermore, we propose the Cross-Layer Attention Module (CLAM) in decoder. In contrast to other attention methods which generate feature attentions from feature itself, we obtain attentions from shallow layers to guide the deep feature. Since the output feature from GPM contains semantic clues from...
作者在 Passage 部分采用 Self-Attention 而不是 Max-Pooling 的主要原因是，Passage 部分通常比较长，用 Self-Attention 可以更好的捕捉单词间长距离关系。2.3 输出层 在卷积层可以得到三个部分的特征向量，输出层采用 BiLinear interaction 预测最终的输出，损失函数为 cross entrophy。3.实验结果 作者对比了三个...