--- title: Emulating the Attention Mechanism in Transformer Models... description: The post discusses the limitations of CNNs in capturing long-range dependencies and global contextual understanding in computer vision tasks. It introduces... canonical: https://daily.dev/posts/emulating-the-attention-mechanism-in-transformer-models-with-a-fully-convolutional-network-u6wgehqit twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Emulating the Attention Mechanism in Transformer Models with a Fully Convolutional Network | daily.dev og:description: The post discusses the limitations of CNNs in capturing long-range dependencies and global contextual understanding in computer vision tasks. It introduces... og:url: https://daily.dev/posts/emulating-the-attention-mechanism-in-transformer-models-with-a-fully-convolutional-network-u6wgehqit og:image: https://api.daily.dev/og/posts/U6WgEhQIT.png og:image:alt: Emulating the Attention Mechanism in Transformer Models with a Fully Convolutional Network og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Emulating the Attention Mechanism in Transformer Models with a Fully Convolutional Network **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 9 min read · 0 upvotes · 0 comments ## Summary The post discusses the limitations of CNNs in capturing long-range dependencies and global contextual understanding in computer vision tasks. It introduces transformers as an alternative architecture that excels in capturing global relationships. To combine the strengths of CNNs and transformers, the post presents Convolutional Self-Attention (CSA), which achieves both local and global feature relations using convolution operations. CSA demonstrates superior performance compared to contemporary transformer models, with faster latency and comparable accuracy when running on TensorRT. It is fully compatible with TensorRT restricted mode. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp), [#computer-vision](https://daily.dev/tags/computer-vision), [#transformers](https://daily.dev/tags/transformers), [#edge-computing](https://daily.dev/tags/edge-computing) [View this post on daily.dev](https://daily.dev/posts/emulating-the-attention-mechanism-in-transformer-models-with-a-fully-convolutional-network-u6wgehqit) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Emulating the Attention Mechanism in Transformer Models with a Fully Convolutional Network","url":"https://daily.dev/posts/emulating-the-attention-mechanism-in-transformer-models-with-a-fully-convolutional-network-u6wgehqit","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/emulating-the-attention-mechanism-in-transformer-models-with-a-fully-convolutional-network-u6wgehqit"},"datePublished":"2024-01-29T17:05:12.258Z","dateModified":"2024-05-09T08:29:02.242Z","description":"The post discusses the limitations of CNNs in capturing long-range dependencies and global contextual understanding in computer vision tasks. 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