<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vision on SoloSoft</title><link>https://www.solosoft.dev/tags/vision/</link><description>Recent content in Vision on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/vision/index.xml" rel="self" type="application/rss+xml"/><item><title>GEMS: General Multimodal Sensing Framework</title><link>https://www.solosoft.dev/post/gems-multimodal-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/gems-multimodal-2026/</guid><description>&lt;p&gt;The real world does not present information in a single modality. We experience it through vision, language, audio, and physical sensation simultaneously, and AI systems that operate in the real world need the same multimodal understanding. &lt;strong&gt;GEMS&lt;/strong&gt; (lcqysl/GEMS on GitHub) &amp;ndash; the General Multimodal Sensing framework &amp;ndash; provides a unified infrastructure for building AI applications that integrate vision, language, audio, and structured data into coherent understanding systems.&lt;/p&gt;
&lt;p&gt;Developed by the lcqysl research team, GEMS addresses one of the most challenging problems in modern AI: how to combine information from different sensory channels into a single, unified representation that can be used for reasoning, decision-making, and interaction. The framework handles modality-specific processing, cross-modal alignment, and multimodal fusion in a modular architecture that supports both research experimentation and production deployment.&lt;/p&gt;</description></item><item><title>MiniCPM-o: Open-Source Multimodal LLM for Vision, Speech, and Text</title><link>https://www.solosoft.dev/post/minicpm-o-multimodal-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/minicpm-o-multimodal-2026/</guid><description>&lt;p&gt;Multimodal AI models that can simultaneously process vision, speech, and text represent the cutting edge of artificial intelligence. OpenAI&amp;rsquo;s GPT-4o demonstrated the potential of this approach, but its closed nature has left the open-source community racing to catch up. &lt;strong&gt;MiniCPM-o&lt;/strong&gt;, developed by OpenBMB (offshoot of Tsinghua University&amp;rsquo;s NLP lab), has achieved a remarkable milestone: it outperforms GPT-4o on single-image understanding benchmarks while matching or exceeding it on speech tasks &amp;ndash; all in an open-source package.&lt;/p&gt;
&lt;p&gt;The project at &lt;a href="https://github.com/OpenBMB/MiniCPM-o"&gt;github.com/OpenBMB/MiniCPM-o&lt;/a&gt; represents a series of multimodal LLMs that extend the MiniCPM family&amp;rsquo;s impressive performance-to-size ratio into the multimodal domain. MiniCPM-o supports full-duplex voice interaction &amp;ndash; meaning it can listen and speak simultaneously, like a natural conversation &amp;ndash; along with image understanding, optical character recognition, and multi-turn dialogue capabilities.&lt;/p&gt;</description></item></channel></rss>