๐ฅ 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |
๐๐ฎ๐-๐ฎ๐ต๐ฒ ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐๐-๐ก๐ฒ๐:Efficient Channel Attention for Deep Convolutional Neural Networks by Tianjin University, China Follow me for a similar post: ๐ฎ๐ณ Ashish Patel Interesting Facts : ๐ธ This paper is published in CVPR 2020 with 1 Citation. ------------------------------------------------------------------- ๐๐บ๐ฎ๐๐ถ๐ป๐ด ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต: https://lnkd.in/e_AiPXka code: https://lnkd.in/e4XDmBD3 ------------------------------------------------------------------- ๐๐ ๐ฃ๐ข๐ฅ๐ง๐๐ก๐๐ ๐ธ Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity.ย ๐ธ To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity.ย ๐ธ Therefore, we propose a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction.ย ๐ธ The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against a backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy.ย ๐ธ We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts. #computervision #artificialintelligence #deeplearning
๐ฅ 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |
2yhttps://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post