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README.md

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 [[paper]](https://arxiv.org/abs/2405.19783) [[project page]](https://2toinf.github.io/IVM/)
 
+### 🔥 IVM has been selected as outstanding paper at MFM-EAI workshop @ICML2024
+
 ## Introduction
 
 We introduce Instruction-guided Visual Masking (IVM), a new versatile visual grounding model that is compatible with diverse multimodal models, such as LMM and robot model. By constructing visual masks for instruction-irrelevant regions, IVM-enhanced multimodal models can effectively focus on task-relevant image regions to better align with complex instructions. Specifically, we design a visual masking data generation pipeline and create an IVM-Mix-1M dataset with 1 million image-instruction pairs. We further introduce a new learning technique, Discriminator Weighted Supervised Learning (DWSL) for preferential IVM training that prioritizes high-quality data samples. Experimental results on generic multimodal tasks such as VQA and embodied robotic control demonstrate the versatility of IVM, which as a plug-and-play tool, significantly boosts the performance of diverse multimodal models.
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 This work is built upon the [LLaVA](https://github.com/haotian-liu/LLaVA) and [SAM](https://github.com/facebookresearch/segment-anything). And we borrow ideas from [LISA](https://github.com/dvlab-research/LISA)
 
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 ## Citation
 
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                 <span class="author-block">✉Corresponding author:</span>
                 <span class="author-block"><a href="mailto:[email protected]">[email protected]</a></span>
             </div>
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+            <style>
+              .accepted {
+                text-align: center;
+                color: #0a283d; /* Dark blue color */
+                font-size: 24px;
+                background-color: #e8f0fe79; /* Light blue background */
+                border: 1px solid #B6D4FE;
+                border-radius: 10px;
+                padding: 20px;
+                box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+            }
+            </style>
+            <div class="accepted">
+              <i class="fas fa-fire icon" style="color: red;"></i>
+               Exciting News! 
+               <div>Our paper has been selected as 
+                <span style="color: red; font-weight: bold;"> outstanding paper </span> 
+
+                at MFM-EAI workshop@ICML2024</div>
+            </div>
 
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