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Beijing Institute of Technology
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Achieving over 90% token reduction in 3D VLMs without sacrificing performance could revolutionize the efficiency of 3D scene understanding.
False alarms in change detection can be drastically reduced by learning to distinguish semantic changes from non-semantic variations through innovative self-distillation techniques.
Geometry-aware distillation enables text-driven video segmentation models to achieve state-of-the-art performance by leveraging 3D geometric knowledge, even from 2D image datasets.
