<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Segmentation on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/segmentation/</link><description>Recent content in Segmentation on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/segmentation/index.xml" rel="self" type="application/rss+xml"/><item><title>Speaker Change Detection</title><link>https://terms-en.ai-term-hub.com/en/terms/speaker_change_detection/</link><pubDate>Sat, 18 Jul 2026 10:16:18 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/speaker_change_detection/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Speaker Change Detection (SCD) is a technique used to pinpoint exact timestamps where one speaker stops talking and another begins. It serves as a preliminary step in diarization, helping to segment continuous audio into homogeneous segments belonging to the same speaker. Algorithms typically analyze spectral changes and voice activity to detect these transitions accurately.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The process of identifying points in an audio stream where the active speaker changes.&lt;/p></description></item><item><title>Sam3 Video</title><link>https://terms-en.ai-term-hub.com/en/terms/sam3_video/</link><pubDate>Sat, 18 Jul 2026 10:14:36 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/sam3_video/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Sam3 Video refers to the application of advanced segmentation models, potentially a hypothetical or specific version of Meta&amp;rsquo;s Segment Anything Model, to video data. It involves tracking objects across frames, maintaining consistent masks over time, and handling occlusions and motion blur. This capability is essential for video editing, autonomous driving perception, and surveillance analysis, where dynamic object segmentation is required rather than static image processing.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>This term likely denotes video segmentation capabilities associated with a third-generation or specific variant of the Segment Anything Model applied to video streams.&lt;/p></description></item><item><title>LocateAnything</title><link>https://terms-en.ai-term-hub.com/en/terms/locateanything/</link><pubDate>Sat, 18 Jul 2026 10:05:43 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/locateanything/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>LocateAnything is a versatile computer vision framework that enables the detection and segmentation of objects in images based on natural language prompts or general priors. It leverages pre-trained foundation models to achieve zero-shot capabilities, allowing users to locate specific items in complex scenes without needing labeled datasets for every new object type. This approach significantly reduces the annotation burden and enhances adaptability in dynamic visual environments.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>An open-source framework designed for zero-shot object localization and segmentation across diverse visual domains without task-specific training.&lt;/p></description></item></channel></rss>