<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PyTorch | Ruize Xia · Artificial Minds, Human Values</title><link>https://portfolio.xiaruize.org/tags/pytorch/</link><atom:link href="https://portfolio.xiaruize.org/tags/pytorch/index.xml" rel="self" type="application/rss+xml"/><description>PyTorch</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 14 Jul 2026 00:00:00 +0000</lastBuildDate><image><url>https://portfolio.xiaruize.org/media/icon_hu_982c5d63a71b2961.png</url><title>PyTorch</title><link>https://portfolio.xiaruize.org/tags/pytorch/</link></image><item><title>Text2Sign</title><link>https://portfolio.xiaruize.org/projects/text2sign/</link><pubDate>Tue, 14 Jul 2026 00:00:00 +0000</pubDate><guid>https://portfolio.xiaruize.org/projects/text2sign/</guid><description>&lt;p>&lt;strong>Text2Sign&lt;/strong> is the public implementation behind the IEEE Access article of the same name. The repository provides a PyTorch training and inference path for short sign-language clips generated from text, using a frozen CLIP text encoder, a 3D backbone, factorized spatiotemporal attention, and DDIM sampling.&lt;/p>
&lt;p>The design target is a single NVIDIA L4 GPU rather than a multi-node cluster. Evaluation uses a signer-disjoint How2Sign split so that appearance memorization is harder to confuse with text-conditioned motion.&lt;/p>
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&lt;li>Code:
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&lt;li>Checkpoint:
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