In this work, we propose Attention Branch Network (ABN), which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for the attention mechanism and is ...
In this work, we propose Attention Branch Network (ABN), which extends the top-down visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for attention mechanism and is trainable for ...
Branch attention在此略过。 Channel & Spatial Attention 这里选择CVPR2017的分类模型Residual Attention Network和CVPR2019的分割模型Dual Attention Network作为代表。 Residual Attention Network对attention机制的使用体现在下图中,i遍历所有的像素,c遍历所有的channel。f_1对像素和channel都进行sigmoid,这是同时使用了...
MBAN: multi-branch attention network for small object detectiondoi:10.7717/peerj-cs.1965Li LiShuaikun GaoFangfang WuXin AnPeerJ Computer Science
Specifically, we first achieve preliminary feature refinement through a backbone network with a non-local attention mechanism. Then, a two-level multi-branch architecture in MBA-Net is proposed with two-level features refinement to obtain aware local discriminative features from the self-attention ...
Selective Kernel Network 在Dynamic Head这个工作中,金字塔结构中不同分辨率的特征也被进行动态加权,也就是下图中的Scale-aware Attention。这个工作还同时采用空间注意力(对应Spatial-aware Attention)和通道注意力(对应Task-aware Attention)。 Dynamic Head
we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce...
每条Local branch处理一个region。每一个bounding box可以有T个regions.然后Global feature 与 Local feature 连接起来获得1024-dim feature,即是HA-CNN的输出 HA结构 灰色为包含BN和ReLU的卷积层棕色表示global average pooling蓝色表示全连接层. Harmonious Attention Network for Person Re-Identification(http://cn.ar...
Firstly, separate pooling operations are performed on each branch, and then their results are concatenated instead of using pixel-wise addition. We have decided to connect the feature maps from different branches in the channel in order to obtain feature maps that preserve both spatial and channel...
Hard regional attention将输出的位置参数加到相应network block,生成T个不同part,然后将其加到local branch。 (三) Cross-Attention Interaction Learning 通过local features 和 global features 跨分支交流使得soft attention 与 hard attention 联合学习更加和谐 \bar{X}_{L}^{(l,k)} = X_{L}^{(l,k)}...