ATTENTION TRANSFER and CBAM - Code World

ATTENTION TRANSFER and CBAM

Others 2021-03-07 17:52:20 views: null

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Origin blog.csdn.net/qq_41872271/article/details/105497332

ATTENTION TRANSFER and CBAM

Attention mechanism CBAM implementation

CBAM attention mechanism - pytorch implementation

Attention mechanism - Convolutional Block Attention Module (CBAM)

CBAM: Convolutional Block Attention Module—— channel attention + spatial attention

[Attention] CBAM mechanism CV in companion -BAM module

Attention mechanism CBAM code implementation (sequel)

Robot Grabbing Series - CBAM Attention Mechanism

Detailed Attention Mechanism in Images (SEBlock | ECABlock | CBAM)

[Deep Learning Attention Mechanism Series] - CBAM Attention Mechanism (with pytorch implementation)

Improved version of ASPP (2): CBAM (convolutional attention module) is added to the ASPP module, namely CBAM_ASPP

Detailed Interpretation of Vernacular (8) ----- CBAM: Convolutional Block Attention Module

YOLOV7 adds CBAM attention mechanism in different positions

Attention module SE, CBAM, ECA three code implementations

Interpretation of Pytorch image processing attention mechanism SENet CBAM ECA module

2018 ECCV „CBAM: Convolutional Block Attention Module“ PyTorch-Implementierung

[Paper notes] CBAM: Convolutional Block Attention Module (convolutional block attention model)

Attention mechanism: based on Yolov8's Triplet attention module, plug and play, the effect is better than cbam and se, and the increase point is obvious

Yolov5/Yolov7 improvement --- attention mechanism: Polarized Self-Attention, the effect spikes CBAM, SE

yolov5 added CBAM, SE, CA, ECA attention mechanism, pure code (22.3.1 also updated)

[Deep Learning] (9) Mixed Domain Attention Mechanism (DANet, CBAM) in CNN, complete with Tensorflow code

YOLOv5-6.1 adds attention mechanism (SE, CBAM, ECA, CA)

Yolov8 improvement --- attention mechanism: CoordAttention, the effect spikes CBAM, SE

[Selected] Highway Intelligent Inspection System: CBAM Spatial Attention Mechanism Improved YOLOv5

Yolov8 improves the CoTAttention attention mechanism, and the effect instantly kills CBAM and SE

Yolov5/Yolov7 improvement --- attention mechanism: SimAM (no parameter Attention) and NAM (based on standardized attention module), the effect spikes CBAM, SE

CVPR2021| 继SE,CBAM后的一种新的注意力机制Coordinate Attention

Optimize and improve the receptive field attention convolution operation (RFAConv) of the YOLOv5 algorithm, and the effect is faster than CBAM and CA, etc.

YoloV8 improvement strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

Pay attention to the shell script transfer parameters, the use of the shift command

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