Single Fundus Image Super-Resolution Via Cascaded Channel-Wise Attention Network

Fan, Zhihao; Dan, Tingting; Yu, Honghua; Liu, Bao-yi; Cai, Hongmin

doi:10.1109/embc44109.2020.9176428

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Attention mechanism proves to be an effective component for CNN to boost the representation performance and improve the predicting results. In general, the attention mechanism can be separated into three different kinds: the channel-wise attention mecha-nism [60], the spatial attention mechanism [61] and the non-local attention mechanism [62]. The channel-wise attention mechanism embeds the image features into a vector and gives different weights to different feature channels.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…The non-local attention mechanism calculates the global relationship of the image feature, and utilizes the matrix multiplication operation to conduct the attention procedure. The attention mechanism has been widely used in different computer vision and image processing tasks, such as image super-resolution [60], image dehazing [63], object detection [61] and image segmentation [62].…”

Section: Attention Mechanismmentioning

confidence: 99%

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Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

Liu

et al. 2023

Processes

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Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

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Section: Attention Mechanismmentioning

confidence: 99%

Section: Attention Mechanismmentioning

confidence: 99%

Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

Liu

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…In recent years, attention mechanism is widely used on various tasks [8][9][10], which only focuses on selective parts of the whole visual space when and where as needed. However, as cross-domain research combining computer vision and natural language processing, relying on visual features alone is still not sufficient to generate high-quality captions, textual information is also crucial for improving model performance.…”

Section: Introductionmentioning

confidence: 99%