Top 24 Bam Bottleneck Attention Module The 149 Correct Answer

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What is attention module?

Attention modules are used to make CNN learn and focus more on the important information, rather than learning non-useful background information. In the case of object detection, useful information is the objects or target class crop that we want to classify and localize in an image.

What is channel attention module?

A Channel Attention Module is a module for channel-based attention in convolutional neural networks. We produce a channel attention map by exploiting the inter-channel relationship of features.

What is attention map?

attention map: a scalar matrix representing the relative importance of layer activations at different 2D spatial locations with respect to the target task. i.e., an attention map is a grid of numbers that indicates what 2D locations are important for a task.

What is attention based convolutional neural network?

In this paper, we propose an attention-based convolutional neural network (ACNN) to learn the global-feature relationships of aligned face images, which aims to decrease the information redundancy among channels and focus on the most informative components of face feature maps.

What are the types of attention?

How do attention models work?

Attention models evaluate inputs to identify the most critical components and assign each of them with a weight. For example, if using an attention model to translate a sentence from one language to another, the model would select the most important words and assign them a higher weight.

What is squeeze and excitation?

The Squeeze-and-Excitation Block is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is: The block has a convolutional block as an input.

What is attention pooling?

An attention pooling layer is used to integrate local representations into the final sentence representation with attention weights.

What is self attention in deep learning?

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence.

What is attention in NLP?

Attention mechanism is one of the recent advancements in Deep learning especially for Natural language processing tasks like Machine translation, Image Captioning, dialogue generation etc. It is a mechanism that is developed to increase the performance of encoder decoder(seq2seq) RNN model.

How is attention calculated?

The attention weights are calculated by normalizing the output score of a feed-forward neural network described by the function that captures the alignment between input at j and output at i.

What are attention based models?

Attention-based models belong to a class of models commonly called sequence-to-sequence models. The aim of these models, as name suggests, it to produce an output sequence given an input sequence which are, in general, of different lengths.

What is the difference between attention and self attention?

The attention mechanism allows output to focus attention on input while producing output while the self-attention model allows inputs to interact with each other (i.e calculate attention of all other inputs wrt one input.

What is the difference between soft and hard attention?

Hard vs Soft attention

in their paper, soft attention is when we calculate the context vector as a weighted sum of the encoder hidden states as we had seen in the figures above. Hard attention is when, instead of weighted average of all hidden states, we use attention scores to select a single hidden state.

What is attention Matrix?

Attention takes two sentences, turns them into a matrix where the words of one sentence form the columns, and the words of another sentence form the rows, and then it makes matches, identifying relevant context. This is very useful in machine translation.

What does attention mean in deep learning?

Attention is an interface connecting the encoder and decoder that provides the decoder with information from every encoder hidden state. With this framework, the model is able to selectively focus on valuable parts of the input sequence and hence, learn the association between them.

What is attention in NLP?

Attention mechanism is one of the recent advancements in Deep learning especially for Natural language processing tasks like Machine translation, Image Captioning, dialogue generation etc. It is a mechanism that is developed to increase the performance of encoder decoder(seq2seq) RNN model.

What is attention in computer vision?

In the context of machine learning, attention is a technique that mimics cognitive attention, defined as the ability to choose and concentrate on relevant stimuli. In other words, attention is a method that tries to enhance the important parts while fading out the non-relevant information.

What is attention in cognitive psychology?

What is Attention? Attention is the ability to choose and concentrate on relevant stimuli. Attention is the cognitive process that makes it possible to position ourselves towards relevant stimuli and consequently respond to it. This cognitive ability is very important and is an essential function in our daily lives.

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PR-163: CNN Attention Networks

PR-163: CNN Attention Networks

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bam bottleneck attention module

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• Summary of article content: Articles about bam bottleneck attention module We propose a simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional … …
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bam bottleneck attention module

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• Summary of article content: Articles about bam bottleneck attention module We propose a simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural … …
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bam bottleneck attention module

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• Summary of article content: Articles about bam bottleneck attention module A new module, Bottleneck Attention Module (BAM), is designed, that can be integrated with any feed-forward CNNs. This module infers an attention … …
• Most searched keywords: Whether you are looking for bam bottleneck attention module A new module, Bottleneck Attention Module (BAM), is designed, that can be integrated with any feed-forward CNNs. This module infers an attention …
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[PDF] BAM: Bottleneck Attention Module | Semantic Scholar

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• Summary of article content: Articles about [PDF] BAM: Bottleneck Attention Module | Semantic Scholar A simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks … …
• Most searched keywords: Whether you are looking for [PDF] BAM: Bottleneck Attention Module | Semantic Scholar A simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks … A simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks, that infers an attention map along two separate pathways, channel and spatial. Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in general deep neural networks. We propose a simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks. Our module infers an attention map along two separate pathways, channel and spatial. We place our module at each bottleneck of models where the downsampling of feature maps occurs. Our module constructs a hierarchical attention at bottlenecks with a number of parameters and it is trainable in an end-to-end manner jointly with any feed-forward models. We validate our BAM through extensive experiments on CIFAR-100, ImageNet-1K, VOC 2007 and MS COCO benchmarks. Our experiments show consistent improvement in classification and detection performances with various models, demonstrating the wide applicability of BAM. The code and models will be publicly available.
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Channel Attention Module Explained | Papers With Code

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• Most searched keywords: Whether you are looking for Channel Attention Module Explained | Papers With Code Updating A Channel Attention Module is a module for channel-based attention in convolutional neural networks. We produce a channel attention map by exploiting the inter-channel relationship of features. As each channel of a feature map is considered as a feature detector, channel attention focuses on ‘what’ is meaningful given an input image. To compute the channel attention efficiently, we squeeze the spatial dimension of the input feature map.

  We first aggregate spatial information of a feature map by using both average-pooling and max-pooling operations, generating two different spatial context descriptors: $\mathbf{F}^{c}_{avg}$ and $\mathbf{F}^{c}_{max}$, which denote average-pooled features and max-pooled features respectively.

  Both descriptors are then forwarded to a shared network to produce our channel attention map $\mathbf{M}_{c} \in \mathbb{R}^{C\times{1}\times{1}}$. Here $C$ is the number of channels. The shared network is composed of multi-layer perceptron (MLP) with one hidden layer. To reduce parameter overhead, the hidden activation size is set to $\mathbb{R}^{C/r×1×1}$, where $r$ is the reduction ratio. After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation. In short, the channel attention is computed as:

  $$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\text{MLP}\left(\text{AvgPool}\left(\mathbf{F}\right)\right)+\text{MLP}\left(\text{MaxPool}\left(\mathbf{F}\right)\right)\right) $$

  $$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{avg}\right)\right) +\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{max}\right)\right)\right) $$

  where $\sigma$ denotes the sigmoid function, $\mathbf{W}_{0} \in \mathbb{R}^{C/r\times{C}}$, and $\mathbf{W}_{1} \in \mathbb{R}^{C\times{C/r}}$. Note that the MLP weights, $\mathbf{W}_{0}$ and $\mathbf{W}_{1}$, are shared for both inputs and the ReLU activation function is followed by $\mathbf{W}_{0}$.

  Note that the channel attention module with just average pooling is the same as the Squeeze-and-Excitation Module.

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Channel Attention Module Explained | Papers With Code

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• Summary of article content: Articles about Channel Attention Module Explained | Papers With Code Updating …
• Most searched keywords: Whether you are looking for Channel Attention Module Explained | Papers With Code Updating A Channel Attention Module is a module for channel-based attention in convolutional neural networks. We produce a channel attention map by exploiting the inter-channel relationship of features. As each channel of a feature map is considered as a feature detector, channel attention focuses on ‘what’ is meaningful given an input image. To compute the channel attention efficiently, we squeeze the spatial dimension of the input feature map.

  We first aggregate spatial information of a feature map by using both average-pooling and max-pooling operations, generating two different spatial context descriptors: $\mathbf{F}^{c}_{avg}$ and $\mathbf{F}^{c}_{max}$, which denote average-pooled features and max-pooled features respectively.

  Both descriptors are then forwarded to a shared network to produce our channel attention map $\mathbf{M}_{c} \in \mathbb{R}^{C\times{1}\times{1}}$. Here $C$ is the number of channels. The shared network is composed of multi-layer perceptron (MLP) with one hidden layer. To reduce parameter overhead, the hidden activation size is set to $\mathbb{R}^{C/r×1×1}$, where $r$ is the reduction ratio. After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation. In short, the channel attention is computed as:

  $$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\text{MLP}\left(\text{AvgPool}\left(\mathbf{F}\right)\right)+\text{MLP}\left(\text{MaxPool}\left(\mathbf{F}\right)\right)\right) $$

  $$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{avg}\right)\right) +\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{max}\right)\right)\right) $$

  where $\sigma$ denotes the sigmoid function, $\mathbf{W}_{0} \in \mathbb{R}^{C/r\times{C}}$, and $\mathbf{W}_{1} \in \mathbb{R}^{C\times{C/r}}$. Note that the MLP weights, $\mathbf{W}_{0}$ and $\mathbf{W}_{1}$, are shared for both inputs and the ReLU activation function is followed by $\mathbf{W}_{0}$.

  Note that the channel attention module with just average pooling is the same as the Squeeze-and-Excitation Module.

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Attention-based convolutional neural network for deep face recognition | SpringerLink

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• Most searched keywords: Whether you are looking for Attention-based convolutional neural network for deep face recognition | SpringerLink Updating Discriminative feature embedding is of essential importance in the field of large scale face recognition. In this paper, we propose an attention-based conv
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GitHub – asdf2kr/BAM-CBAM-pytorch: Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18)

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• Summary of article content: Articles about GitHub – asdf2kr/BAM-CBAM-pytorch: Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18) Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18) – GitHub … …
• Most searched keywords: Whether you are looking for GitHub – asdf2kr/BAM-CBAM-pytorch: Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18) Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18) – GitHub … Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18) – GitHub – asdf2kr/BAM-CBAM-pytorch: Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18)
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Lunit

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bam bottleneck attention module

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• Summary of article content: Articles about bam bottleneck attention module PARK, WOO, LEE, KWEON: BOTTLENECK ATTENTION MODULE. 1. BAM: Bottleneck Attention Module. Jongchan Park*†1. Sanghyun Woo*2. Joon-Young Lee3. In So Kweon2. …
• Most searched keywords: Whether you are looking for bam bottleneck attention module PARK, WOO, LEE, KWEON: BOTTLENECK ATTENTION MODULE. 1. BAM: Bottleneck Attention Module. Jongchan Park*†1. Sanghyun Woo*2. Joon-Young Lee3. In So Kweon2.
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bam bottleneck attention module

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• Summary of article content: Articles about bam bottleneck attention module Official PyTorch code for “BAM: Bottleneck Attention Module (BMVC2018)” and “CBAM: Convolutional Block Attention Module (ECCV2018)” – GitHub … …
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Channel Attention Module Explained

A Channel Attention Module is a module for channel-based attention in convolutional neural networks. We produce a channel attention map by exploiting the inter-channel relationship of features. As each channel of a feature map is considered as a feature detector, channel attention focuses on ‘what’ is meaningful given an input image. To compute the channel attention efficiently, we squeeze the spatial dimension of the input feature map.

We first aggregate spatial information of a feature map by using both average-pooling and max-pooling operations, generating two different spatial context descriptors: $\mathbf{F}^{c}_{avg}$ and $\mathbf{F}^{c}_{max}$, which denote average-pooled features and max-pooled features respectively.

Both descriptors are then forwarded to a shared network to produce our channel attention map $\mathbf{M}_{c} \in \mathbb{R}^{C\times{1}\times{1}}$. Here $C$ is the number of channels. The shared network is composed of multi-layer perceptron (MLP) with one hidden layer. To reduce parameter overhead, the hidden activation size is set to $\mathbb{R}^{C/r×1×1}$, where $r$ is the reduction ratio. After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation. In short, the channel attention is computed as:

$$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\text{MLP}\left(\text{AvgPool}\left(\mathbf{F}\right)\right)+\text{MLP}\left(\text{MaxPool}\left(\mathbf{F}\right)\right)\right) $$

$$ \mathbf{M_{c}}\left(\mathbf{F}\right) = \sigma\left(\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{avg}\right)\right) +\mathbf{W_{1}}\left(\mathbf{W_{0}}\left(\mathbf{F}^{c}_{max}\right)\right)\right) $$

where $\sigma$ denotes the sigmoid function, $\mathbf{W}_{0} \in \mathbb{R}^{C/r\times{C}}$, and $\mathbf{W}_{1} \in \mathbb{R}^{C\times{C/r}}$. Note that the MLP weights, $\mathbf{W}_{0}$ and $\mathbf{W}_{1}$, are shared for both inputs and the ReLU activation function is followed by $\mathbf{W}_{0}$.

Note that the channel attention module with just average pooling is the same as the Squeeze-and-Excitation Module.

Attention-based convolutional neural network for deep face recognition

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asdf2kr/BAM-CBAM-pytorch: Pytorch implementation of BAM(“BAM: Bottleneck Attention Module”, BMVC18) and CBAM(“CBAM: Convolutional Block Attention Module”, ECCV18)

BAM & CBAM Pytorch

Pytorch implementation of BAM and CBAM.

BAM & CBAM Pytorch

This code purpose to evaluate of popular attention model architectures, such as BAM, CBAM on the CIFAR dataset.

Park J, Woo S, Lee J Y, Kweon I S. BAM: Bottleneck Attention Module. 2018. BMVC2018(Oral)

Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional Block Attention Module. 2018. ECCV2018

Architecture

BAM

CBAM

Getting Started

$ git clone https://github.com/asdf2kr/BAM-CBAM-pytorch.git $ cd BAM-CBAM-pytorch $ python main.py –arch bam (default: bam network based on resnet50)

Performance

The table below shows models, dataset and performances

Model Backbone Dataset Top-1 Top-5 Size ResNet resnet50 CIFAR-100 78.93% – 23.70M BAM resnet50 CIFAR-100 79.62% – 24.06M CBAM resnet50 CIFAR-100 81.02% – 26.23M

Reference

Official PyTorch code

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