Specifically, we propose a novel channel-wise feature attention mechanism, which is integrated into the pipeline of a well-known convolutional neural network based visual tracking algorithm. It is crucial to represent the object robustly. Due to the representative feature, the tracking performance is ...
Electroencephalography (EEG)-based emotion recognition has gained widespread attention recently. Although many deep learning methods have been proposed, it is still challenging to simultaneously fuse information in the time–frequency–spatial domain. This paper proposes an attention mechanism-guided dual-...
这个是channel-wise的feature attention,下面是展开之后的样子: (注意上图少画了soft-shrinkage,作者在 HD 之后使用了它) 上图中的d,作者采用的是16,所以导致经过 HD 之后获得了唯一一个channel为4的feature map,其他所有层的channel都是64的。 ⚠️注意:最后的那个相乘是element-wise的,由于两者的size不同...
We designed an attention detection head based on attention mechanisms, enhancing the UAV’s ability to focus on attention regions amidst complex backgrounds, thereby bolstering the reliability of urban UAV patrols. We constructed a UAV image dataset based on the characteristics of urban UAV patrols, ...
we aim to propose a novel attention mechanism by taking into account the two factors: For the first factor, inspired by the CapsuleNet [11] where the grouped sub-features can represent the instantiation parameters of a specific type of en- tity, we propose a group-...
weghts Feature Extractor Siamese Texture Encoder Decoder 3 × 3 Conv GCM Relation Network DirConv Unit DAM : Directionality-Aware Module GCM : Global Context Module Concat Operation Channel-Wise Attention 来自 Semantic Scholar 喜欢 0 阅读量: 16 作者:...
crucial channels and geographical areas, the DSUNET model employs a dual attention mechanism. Let S represent spatial attention, and C represent channel-wise attention. The model combines these attentions to produce the final attention A:(3)A=C⊙SIn this case, ⊙ stands for element-wise ...
The structure of context attention block. Full size image Weight-based feature fusion block For a feature pyramid, the information in the same spatial location of multi-scale feature maps may be inconsistent. Therefore, the feature fusion block, which directly uses the element-wise addition or con...
where\(\oplus \)represents element-wise addition. To integrate the global context information of the three output tensors and restore the channel number ofOto the same asXandY, we introduce a 1\(\times \)1 convolutional layer\(P_2\); then through a Sigmoid activation function\(\sigma \)...
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 mechanism. ...