Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly - Code World

Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly

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Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly

Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly

EMA: An Efficient Multi-Scale Attention Module Based on Cross-Spatial Learning

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

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

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

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

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

Yolov5/Yolov7 improvement --- attention mechanism: ShuffleAttention, ECA, EffectiveSE, SE

Improve YOLOv7 | Add [CA] [ECA] [CBAM] [SE] attention mechanism in ELAN module and ELAN-H module | Attach detailed structure diagram

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 --- attention mechanism: the introduction of GCNet, GENet, the effect is better than SENet

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.71] attention mechanism Expectation-Maximization Attention (EMA module)

Improve YOLOv8 | Plug and Play | Add [SE] [CBAM] [ECA] [CA] attention mechanism in the C2F module | Attach a detailed structure diagram

Gain point artifact: small target performance improvement based on Yolov5/Yolov7

YOLOV5 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C3 block, effectively increase points

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

YOLOV7 adds CBAM attention mechanism in different positions

Interpretation of Pytorch image processing attention mechanism SENet CBAM ECA module

YOLOv5 Improvement | 2023 Attention Chapter | iRMB Inverted Residual Block Attention Mechanism (Lightweight Attention Mechanism)

ECA attention mechanism

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

Yolov8 개선---주의 메커니즘: ICASSP2023 EMA는 교차 공간 학습의 효율적인 다중 규모 주의를 기반으로 하며 효과는 ECA, CBAM, CA보다 우수합니다 | 작은 목표는 크게 증가했습니다

YOLOv5 Improvement Series (20) - Add BiFormer Attention Mechanism (CVPR2023|Small Goal Increase Point Artifact)

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

YOLOV8 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C2f module, effectively increase points

Attention mechanism CBAM implementation

yolov5+bytetrack target tracking, the effect is better than deepsort

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

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