<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Peer-Reviewed | Wenjie Shu</title><link>https://wenjieshu.github.io/tags/peer-reviewed/</link><atom:link href="https://wenjieshu.github.io/tags/peer-reviewed/index.xml" rel="self" type="application/rss+xml"/><description>Peer-Reviewed</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://wenjieshu.github.io/media/icon_hu_187844eed2f8f505.png</url><title>Peer-Reviewed</title><link>https://wenjieshu.github.io/tags/peer-reviewed/</link></image><item><title>AlignVid: Training-Free Attention Scaling for Semantic Fidelity in Text-Guided Image-to-Video Generation</title><link>https://wenjieshu.github.io/publications/alignvid/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/alignvid/</guid><description>&lt;p&gt;Accepted at ICML 2026.&lt;/p&gt;</description></item><item><title>LoopViT: Scaling Visual ARC with Looped Transformers</title><link>https://wenjieshu.github.io/publications/loopvit/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/loopvit/</guid><description>&lt;p&gt;Accepted at EMNLP 2026 Findings.&lt;/p&gt;
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Figure: LoopViT overview.
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&lt;/div&gt;</description></item><item><title>ScalingAR: Scaling Confidence for Autoregressive Image Generation</title><link>https://wenjieshu.github.io/publications/go-with-your-gut/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/go-with-your-gut/</guid><description>&lt;p&gt;Accepted at ICML 2026.&lt;/p&gt;
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Figure: Confidence scaling overview.
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&lt;/div&gt;</description></item><item><title>Show, Don't Tell: Morphing Latent Reasoning into Image Generation</title><link>https://wenjieshu.github.io/publications/latentmorph/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/latentmorph/</guid><description>&lt;p&gt;Accepted at ICML 2026.&lt;/p&gt;
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Figure: LatentMorph overview.
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&lt;/div&gt;</description></item><item><title>ThinkVid: Benchmarking Visual Reasoning in Video Generative Models</title><link>https://wenjieshu.github.io/publications/thinkvid/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/thinkvid/</guid><description>&lt;p&gt;Accepted at CVPR 2026.&lt;/p&gt;</description></item><item><title>CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening</title><link>https://wenjieshu.github.io/publications/journal-article/</link><pubDate>Mon, 01 Jul 2024 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/journal-article/</guid><description>&lt;p&gt;This work introduces CMT and a hybrid frequency-domain loss to advance pansharpening quality.&lt;/p&gt;
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Figure: CMT modulation overview.
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&lt;/div&gt;</description></item><item><title>Exploring the Low-Pass Filtering Behavior in Image Super-Resolution</title><link>https://wenjieshu.github.io/publications/conference-paper/</link><pubDate>Wed, 01 May 2024 00:00:00 +0000</pubDate><guid>https://wenjieshu.github.io/publications/conference-paper/</guid><description>&lt;p&gt;This work studies why SR models exhibit low-pass tendencies and discusses remedies.&lt;/p&gt;</description></item></channel></rss>