Day-45 Computer Vision Learning CBAM — Convolutional Block Attention Module (Image Classification) by Korea Advanced Institute of Science and Technology, and Adobe Research Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2018 #ECCV, which has already got over 1434 citations. 🔸 CBAM Outperforms SENet on top of MobileNetV1, ResNeXt, WRN, & ResNet, WRN 🔸 It can be seen as an extension of BAM in 2018 BMVC. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eQT7mHg official Code : https://bit.ly/3tViOnb tensorflow: https://bit.ly/3tZinsc pytorch: https://bit.ly/2OCHil7 keras: https://bit.ly/3dfPrq1 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Attention mechanism is to hope that the network can automatically learn the places that need attention in pictures or text sequences. 🔸 Given an intermediate feature map, BAM sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. #computervision #artificialintelligence #technology
Thank you for sharing
🔸 CBAM can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs. For previous post visit this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post #deeplearning #machinelearning #innovation
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5yGreat. Thanks for sharing . Can you please your implementation code any of the data set? That would be helpful for someone new in this field