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Texas A&M University, City University of Hong Kong
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Kullback鈥揕eibler divergence is the secret sauce that ensures density-level and sample-conditioned objectives align perfectly in generative modeling.
GEAR-VLA achieves a remarkable 90.1% success rate in universal grasping tasks, showcasing its ability to generalize across unseen objects and diverse robot embodiments.
MLLMs can be distilled into lightweight arbiters that dramatically improve the robustness of composed image retrieval by disentangling noisy training signals.
HABIT achieves superior image retrieval performance by simulating human habit formation, effectively tackling the Noise Triplet Correspondence problem that plagues traditional methods.
Recovering static 3D scenes from monocular video with dynamic objects gets a boost: GA-GS leverages diffusion models to inpaint occluded regions, outperforming existing methods, especially in scenarios with large-scale occlusions.
