<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Computer Vision — The Latent Daily</title><description>Images, video and visual understanding.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/computer-vision.xml" rel="self" type="application/rss+xml"/><item><title>How diffusion models turn noise into images</title><link>https://thelatentdaily.com/articles/how-diffusion-models-generate-images/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-diffusion-models-generate-images/</guid><description>Stable Diffusion, FLUX and most image generators rely on diffusion. A clear explanation of forward noising, learned denoising, text guidance and latent space.</description><pubDate>Sun, 18 Oct 2026 05:43:00 GMT</pubDate><category>Explainers</category><category>explainer</category><category>diffusion</category><category>image-generation</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Batch normalization explained: the trick that made deep networks easier to train</title><link>https://thelatentdaily.com/research/batch-normalization-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/batch-normalization-paper-explained/</guid><description>The 2015 batch normalization paper let networks train faster and deeper by normalizing layer inputs. How it works, why it helps, and what transformers use instead.</description><pubDate>Sun, 18 Oct 2026 05:21:00 GMT</pubDate><category>Machine Learning</category><category>research</category><category>deep-learning</category><category>normalization</category><category>training</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>ResNet explained: the skip connection that let networks go 100+ layers deep</title><link>https://thelatentdaily.com/research/resnet-deep-residual-learning-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/resnet-deep-residual-learning-explained/</guid><description>The 2015 ResNet paper solved the degradation problem with residual connections and won ImageNet. How skip connections work and why every transformer uses them.</description><pubDate>Sun, 18 Oct 2026 05:18:00 GMT</pubDate><category>Computer Vision</category><category>research</category><category>resnet</category><category>computer-vision</category><category>deep-learning</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Vision Transformer (ViT) explained: an image is worth 16×16 words</title><link>https://thelatentdaily.com/research/vision-transformer-vit-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/vision-transformer-vit-explained/</guid><description>In 2020, Google showed that a plain transformer applied to image patches can rival convolutional networks. How ViT works, why it needs lots of data and what it enabled.</description><pubDate>Sun, 18 Oct 2026 05:17:00 GMT</pubDate><category>Computer Vision</category><category>research</category><category>vision-transformer</category><category>computer-vision</category><category>transformer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>CLIP explained: how OpenAI taught a model to connect images and words</title><link>https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</guid><description>CLIP learned from 400 million image-caption pairs to match pictures with text, enabling zero-shot image classification and powering text-to-image generators.</description><pubDate>Sun, 18 Oct 2026 05:16:00 GMT</pubDate><category>Multimodal AI</category><category>research</category><category>clip</category><category>multimodal</category><category>computer-vision</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How self-driving cars see the world: cameras, lidar and radar</title><link>https://thelatentdaily.com/articles/how-self-driving-cars-see-cameras-lidar-radar/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-self-driving-cars-see-cameras-lidar-radar/</guid><description>The sensors behind autonomous vehicles, what each does best, how AI fuses them into a picture of the road, and the debate between camera-only and multi-sensor designs.</description><pubDate>Sun, 18 Oct 2026 04:31:00 GMT</pubDate><category>Autonomous Vehicles &amp; Drones</category><category>autonomous-vehicles</category><category>sensors</category><category>lidar</category><category>computer-vision</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How AI is used in healthcare today: from scans to clinical notes</title><link>https://thelatentdaily.com/articles/how-ai-is-used-in-healthcare/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-ai-is-used-in-healthcare/</guid><description>Medical imaging, clinical documentation, triage, drug discovery and patient tools: where AI is already used in healthcare, what the evidence shows and the open risks.</description><pubDate>Sun, 18 Oct 2026 04:07:00 GMT</pubDate><category>Healthcare &amp; Medicine</category><category>healthcare</category><category>medical-imaging</category><category>clinical-ai</category><category>regulation</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How AI is changing sports: officiating, tactics, training and fans</title><link>https://thelatentdaily.com/articles/how-ai-is-changing-sports/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-ai-is-changing-sports/</guid><description>Hawk-Eye and semi-automated offside, tracking data, tactical analysis, injury prevention, scouting and broadcasting: how AI is used across professional sports.</description><pubDate>Sun, 18 Oct 2026 03:28:00 GMT</pubDate><category>Sports</category><category>sports</category><category>computer-vision</category><category>analytics</category><category>officiating</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>