Thirdly, SFAM aggregates the multi-level multi-scale features by a scale-wise feature concatenation operation and a channel-wise attention mechanism.其中Xbase表示基本特征,xli表示在第l个TUM中具有第i个尺度的特征,L表示TUM的数量,T1表示第l个TUM处理,并且F表示FFMv1处理。 第三,SFAM通过按比例缩放的...
Thirdly, SFAM aggregates the multi-level multi-scale features by a scale-wise feature concatenation operation and a channel-wise attention mechanism. 其中Xbase 表示基本特征,xli 表示在第l 个TUM 中具有第i 个尺度的特征,L 表示TUM 的 数量,T1 表示第l 个TUM 处理,并且F 表示FFMv1 处理。 第三,...
Pool C Linear Mean Size and Position C Per-point Direction Distance Confidence 3D graph max pooling Global Feature ORL +Multi-layer Perceptron C Point-wise Feature Concatenation Element-wise Summation Pose and Size Estimation z x y Symmetry- Aware...
其中,\Phi_{Mult}、\Phi_{sub}和\Phi_{cat}都有卷积层和ReLu层组成,[ , ]代表channel-wise concatenation。然后将f_{q}^{c}输入二元分类和bbox回归层,以预测候选框。所提出的特征融合网络可以自然地用卷积层实现,并且计算效率高,可以提高对novel类候选框的召回率。 4. Proposal Classification and Re...
In this paper, we propose a novel approach for VQA task based on Swin Transformer with improved spatio- temporal feature fusion, which precisely mines the stage- wise feature concatenation and provides competitive assess- ment performance. In addition, we...
继续concatenate输出. 文章提到了两种融合信息的方式:elemnet-wise sum,concatenation 实验部分也对比了两种融合方式的结果:C指代Concatenation results 我们可以看到在something数据集上的结果也超过了TRN.可见加入了运动信息的网络就是炫酷了. 思考 文章中计算两帧之间的运动信息的的方式的思想类似于TSN文章中使用的RGB-di...
feature with the i-th scale in the l-th TUM, L denotes the number of TUMs, Tl denotes the l-th TUM processing, and F denotes FFMv1 processing. Thirdly, SFAM aggregates the multi-level multi-scale features by a scale-wise feature concatenation operation and a channel-wise attention ...
In this paper, we show that such strategies can be integrated into Convolutional Neural Network (CNN) architecture via average pooling and channel-wise feature concatenation. Shallow networks with feature aggregation at multi-resolution enables the traditional cascade framework to tackle the challenging ...
在目前的目标检测算法中,为了充分利用高层特征的语义信息和底层特征的细粒度特征,采用最多也是较好的特征融合方式一般是FPN架构方式,但是无论是类似于YOLOv3还是RetinaNet他们多用concatenation或者element-wise这种直接衔接或者相加的方式,论文作者认为这样并不能充分利用不同尺度的特征。所以提出一种新的融合方式来替代...
Ablation Study之 Adaptively Spatial Feature Fusion:40.6 AP & 46fps.从下表中可以看到,element-wise sum and concatenation与ASFF一样提高了APS和APM的准确性,但是它们都大大降低了APL的性能。这表明,特征金字塔中不同级别的不一致性给训练过程带来了负面影响,从而使金字塔特征表示的潜力无法得到充分利用。