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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Brenda Potts</author_name><author_url>https://www.microsoft.com/en-us/research/people/v-brepotts/</author_url><title>Instruction-Guided Visual Masking - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="7R047JiguF"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/instruction-guided-visual-masking/"&gt;Instruction-Guided Visual Masking&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/instruction-guided-visual-masking/embed/#?secret=7R047JiguF" width="600" height="338" title="&#x201C;Instruction-Guided Visual Masking&#x201D; &#x2014; Microsoft Research" data-secret="7R047JiguF" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targeted local region of an image. To achieve more accurate and nuanced multimodal instruction following, we introduce Instruction-guided Visual Masking (IVM), a new versatile visual grounding model that is compatible with diverse [&hellip;]</description></oembed>
