update research

research.html

@@ -94,7 +94,7 @@ <h2><hr>Causal Representation Learning</h2>
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     <h2><hr>Generative Models</h2>
-            <img src="facegeneration.jpg" align="right" border="0" width=300/>
+            <img src="facegeneration.jpg" align="right" border="0" width=200/>
 
         Generative models, including GPT, GANs, and diffusion models, wield significant neural network capabilities to faithfully replicate intricate distributions found in real data. Our research emphasis lies in crafting generative models that are not only data-efficient but also computationally efficient. Moreover, delving into the realm of causal generative models, our core interest centers on developing models that mimic the data generation process while affording controllable and nuanced generations. Lastly, our curiosity extends to exploring generative models tailored for diverse data types, spanning images, text, 3D human motion, and beyond.
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@@ -126,7 +126,7 @@ <h2><hr>Generative Models</h2>
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     <h2><hr>3D Vision</h2>
-    <img src="3D.png" align="right" border="0" width=350/>
+    <img src="3D.png" align="right" border="0" width=320/>
 
     The realm of 3D vision is dedicated to modeling and comprehending the visual world, with a primary focus on deducing three-dimensional structures from two-dimensional images. Our research explores machine learning approaches for various facets of 3D vision, including depth estimation, novel view synthesis, feature matching, SLAM, and 3D model retrieval.    
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